{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import numpy as np # get rid of this eventually\n",
    "import argparse\n",
    "from jax import jit\n",
    "from jax.experimental.ode import odeint\n",
    "from functools import partial # reduces arguments to function by making some subset implicit\n",
    "\n",
    "from jax.experimental import stax\n",
    "from jax.experimental import optimizers\n",
    "\n",
    "import os, sys, time\n",
    "sys.path.append('..')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up LNN:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.path.append('../experiment_dblpend/')\n",
    "\n",
    "from lnn import raw_lagrangian_eom\n",
    "from data import get_dataset\n",
    "from models import mlp as make_mlp\n",
    "from utils import wrap_coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.path.append('../hyperopt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from HyperparameterSearch import learned_dynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from HyperparameterSearch import extended_mlp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ObjectView(object):\n",
    "    def __init__(self, d): self.__dict__ = d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from data import get_trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from data import get_trajectory_analytic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from physics import analytical_fn\n",
    "\n",
    "vfnc = jax.jit(jax.vmap(analytical_fn))\n",
    "vget = partial(jax.jit, backend='cpu')(jax.vmap(partial(get_trajectory_analytic, mxsteps=100), (0, None), 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle as pkl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Here are our model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "args = ObjectView({'dataset_size': 200,\n",
    " 'fps': 10,\n",
    " 'samples': 100,\n",
    " 'num_epochs': 80000,\n",
    " 'seed': 0,\n",
    " 'loss': 'l1',\n",
    " 'act': 'softplus',\n",
    " 'hidden_dim': 30,\n",
    " 'output_dim': 1,\n",
    " 'layers': 3,\n",
    " 'n_updates': 1,\n",
    " 'lr': 0.001,\n",
    " 'lr2': 2e-05,\n",
    " 'dt': 0.1,\n",
    " 'model': 'gln',\n",
    " 'batch_size': 68,\n",
    " 'l2reg': 5.7e-07,\n",
    "})\n",
    "# args = loaded['args']\n",
    "rng = jax.random.PRNGKey(args.seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jax.experimental.ode import odeint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from HyperparameterSearch import new_get_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "vfnc = jax.jit(jax.vmap(analytical_fn, 0, 0))\n",
    "vget = partial(jax.jit, backend='cpu')(jax.vmap(partial(get_trajectory_analytic, mxsteps=100), (0, None), 0))\n",
    "\n",
    "batch = 60\n",
    "\n",
    "@jax.jit\n",
    "def get_derivative_dataset(rng):\n",
    "    # randomly sample inputs\n",
    "\n",
    "    y0 = jnp.concatenate([\n",
    "        jax.random.uniform(rng, (batch, 2))*2.0*np.pi,\n",
    "        (jax.random.uniform(rng+1, (batch, 2))-0.5)*10*2\n",
    "    ], axis=1)\n",
    "    \n",
    "    return y0, vfnc(y0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_params = None\n",
    "best_loss = np.inf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_random_params, nn_forward_fn = extended_mlp(args)\n",
    "import HyperparameterSearch\n",
    "HyperparameterSearch.nn_forward_fn = nn_forward_fn\n",
    "_, init_params = init_random_params(rng+1, (-1, 4))\n",
    "rng += 1\n",
    "model = (nn_forward_fn, init_params)\n",
    "opt_init, opt_update, get_params = optimizers.adam(args.lr)\n",
    "opt_state = opt_init(init_params)\n",
    "from jax.tree_util import tree_flatten\n",
    "from HyperparameterSearch import make_loss, train\n",
    "from copy import deepcopy as copy\n",
    "# train(args, model, data, rng);\n",
    "from jax.tree_util import tree_flatten\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Current std:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jax.ops import index_update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "HyperparameterSearch.nn_forward_fn = nn_forward_fn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's score the qdotdot output over normally distributed input for 256 batch size:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jax import grad, vmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal = True\n",
    "n = 256\n",
    "\n",
    "@jax.jit\n",
    "def custom_init(stds, rng2):\n",
    "    new_params = []\n",
    "    i = 0\n",
    "    for l1 in init_params:\n",
    "        if (len(l1)) == 0: new_params.append(()); continue\n",
    "        new_l1 = []\n",
    "        for l2 in l1:\n",
    "            if len(l2.shape) == 1:\n",
    "                new_l1.append(jnp.zeros_like(l2))\n",
    "            else:\n",
    "                if normal:\n",
    "                    new_l1.append(jax.random.normal(rng2, l2.shape)*stds[i])\n",
    "#                     n1 = l2.shape[0]\n",
    "#                     n2 = l2.shape[1]\n",
    "#                     power = stds[0]\n",
    "#                     base_scale = stds[1]\n",
    "#                     s = base_scale/(n1+n2)**power\n",
    "#                     new_l1.append(jax.random.normal(rng2, l2.shape)*s)\n",
    "                else:\n",
    "                    new_l1.append(jax.random.uniform(rng2, l2.shape, minval=-0.5, maxval=0.5)*stds[i])\n",
    "                rng2+=1\n",
    "                i += 1\n",
    "\n",
    "        new_params.append(new_l1)\n",
    "        \n",
    "    return new_params\n",
    "\n",
    "@jax.jit\n",
    "def j_score_init(stds, rng2):\n",
    "    \n",
    "    new_params = custom_init(stds, rng2)\n",
    "    \n",
    "    rand_input = jax.random.normal(rng2, [n, 4])\n",
    "    rng2 += 1\n",
    "\n",
    "    outputs = jax.vmap(\n",
    "        partial(\n",
    "        raw_lagrangian_eom,\n",
    "        learned_dynamics(new_params)))(rand_input)[:, 2:]\n",
    "\n",
    "    #KL-divergence to mu=0, std=1:\n",
    "    mu = jnp.average(outputs, axis=0)\n",
    "    std = jnp.std(outputs, axis=0)\n",
    "    \n",
    "    KL = jnp.sum((mu**2 + std**2 - 1)/2.0  - jnp.log(std))\n",
    "    \n",
    "    \n",
    "    def total_output(p):\n",
    "        return vmap(partial(raw_lagrangian_eom, learned_dynamics(p)))(rand_input).sum()\n",
    "\n",
    "    d_params = grad(total_output)(new_params)\n",
    "    \n",
    "    for l1 in d_params:\n",
    "        if (len(l1)) == 0: continue\n",
    "        new_l1 = []\n",
    "        for l2 in l1:\n",
    "            if len(l2.shape) == 1: continue\n",
    "            \n",
    "            mu = jnp.average(l2)\n",
    "            std = jnp.std(l2)\n",
    "            KL += (mu**2 + std**2 - 1)/2.0 - jnp.log(std)\n",
    "            \n",
    "    #HACK\n",
    "#     KL += jnp.sum(stds**2)\n",
    "    return jnp.log10(KL)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "cur_std = jnp.array(\n",
    "[ 0.01]*(args.layers+1)\n",
    ")\n",
    "\n",
    "rng2 = jax.random.PRNGKey(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DeviceArray(12.223649, dtype=float32)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j_score_init(cur_std, rng2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @jax.jit\n",
    "\n",
    "vv = jax.jit(vmap(j_score_init, (None, 0), 0))\n",
    "\n",
    "rng2 = jax.random.PRNGKey(0)\n",
    "def score_init(stds):\n",
    "    global rng2\n",
    "    stds = jnp.array(stds)\n",
    "    stds = jnp.exp(stds)\n",
    "    q75, q50, q25 = np.percentile(vv(stds, jax.random.split(rng2, num=10)), [75, 50, 25])\n",
    "    rng2 += 30\n",
    "    \n",
    "\n",
    "    return q50, q75-q25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10.72271728515625, 3.0607597827911377)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_init(cur_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from bayes_opt import BayesianOptimization\n",
    "\n",
    "# # Bounded region of parameter space\n",
    "pbounds = {'s%d'%(i,): (-15, 15) for i in range(len(cur_std))}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bb(**kwargs):\n",
    "    out, std = score_init([kwargs[q] for q in ['s%d'%(i,) for i in range(len(cur_std))]])\n",
    "#     if out is None or not out > -30:\n",
    "#         return -30.0\n",
    "    return -out, std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fit the best distribution:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's redo that with Bayes:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bayesian:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Old stuff:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hyperopt\n",
    "from hyperopt import hp, fmin, tpe, Trials\n",
    "\n",
    "\n",
    "def run_trial(args):\n",
    "    loss, std = bb(**args)\n",
    "    if loss == np.nan:\n",
    "        return {\n",
    "        'status': 'fail', # or 'fail' if nan loss\n",
    "        'loss': np.inf\n",
    "    }\n",
    "\n",
    "    return {\n",
    "        'status': 'ok', # or 'fail' if nan loss\n",
    "        'loss': -loss,\n",
    "        'loss_variance': std,\n",
    "    }\n",
    "\n",
    "\n",
    "#TODO: Declare your hyperparameter priors here:\n",
    "space = {\n",
    "    **{'s%d'%(i,): hp.normal('s%d'%(i,), -2, 5) for i in range(len(cur_std)-1)\n",
    "    },\n",
    "    **{'s%d'%(len(cur_std)-1,): hp.normal('s%d'%(len(cur_std)-1,), 3, 8)}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "trials = Trials()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5000/5000 [02:31<00:00, 33.10trial/s, best loss: 5.579362630844116]\n"
     ]
    }
   ],
   "source": [
    "best = fmin(run_trial,\n",
    "    space=space,\n",
    "    algo=tpe.suggest,\n",
    "    max_evals=5000,\n",
    "    trials=trials,\n",
    "    verbose=1\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5000"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def k(t):\n",
    "    if 'loss' not in t['result']:\n",
    "        return np.inf\n",
    "    return t['result']['loss']\n",
    "\n",
    "sorted_trials = sorted(trials.trials, key=k)\n",
    "len(trials.trials)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.83585603, -1.01970397, -1.08897794, 16.29924023])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = np.array(\n",
    "    [[s['misc']['vals']['s%d'%(i,)][0] for i in range(len(cur_std))] for s in sorted_trials[:100]]\n",
    ")\n",
    "q[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 layers, 1000 hidden: {(4, 1000), (1000, 1000), (1000, 1000), (1000, 1)}\n",
    "\n",
    "## median top 10/2000: array([-1.47842217, -4.37217279, -3.37083752, 11.13480387])\n",
    "\n",
    "(unconverged)\n",
    "\n",
    "## 4 layers, 100 hidden:  {(4, 100), (100, 100), (100, 100), (100, 1)}\n",
    "\n",
    "## median top 30/5000: array([-1.70680816, -2.40340615, -2.17201716, 10.55268474])\n",
    "\n",
    "(unconverged)\n",
    "\n",
    "## 3 layers, 100 hidden:\n",
    "\n",
    "## median top 100/7000: array([-1.69875614, -2.74589338,  3.75818009])\n",
    "\n",
    "(unverged converged)\n",
    "\n",
    "## 3 layers, 30 hidden:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use Eureqa to get the scalings!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_data = np.array(\n",
    "[\n",
    "    [t['misc']['vals']['s%d'%(i,)][0] for i in range(len(cur_std))] + [t['result']['loss']]\n",
    "for t in trials.trials if 'loss' in t['result'] and np.isfinite(t['result']['loss'])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.save('sdata.npy', simple_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.gaussian_process import GaussianProcessRegressor, kernels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "gp = GaussianProcessRegressor(alpha=3, n_restarts_optimizer=20, normalize_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.579362630844116"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_data[:, -1].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianProcessRegressor(alpha=3, copy_X_train=True, kernel=None,\n",
       "                         n_restarts_optimizer=20, normalize_y=True,\n",
       "                         optimizer='fmin_l_bfgs_b', random_state=None)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.fit(simple_data[:, :-1], simple_data[:, -1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 30, array([-2.12770715, -1.99764457, -1.29472256,  6.1514019 ]))"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args.layers+1, args.hidden_dim, q[gp.predict(q).argmin()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New runs with noise added:\n",
    "\n",
    "## layers, hidden, log(std): (4, 30, array([-2.12770715, -1.99764457, -1.29472256,  6.1514019 ]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## predicted with GP for 3 layers, 100 hidden, {(4, 100), (100, 100), (100, 1)}\n",
    "\n",
    "array([-1.95669793, -2.39555616,  1.92755129])\n",
    "\n",
    "## predicted with GP for 3 layers, 50 hidden, {(4, 50), (50, 50), (50, 1)}\n",
    "\n",
    "array([-1.77223004, -3.2154843 , 10.38542243])\n",
    "\n",
    "\n",
    "## predicted with GP for 3 layers, 30 hidden:\n",
    "\n",
    "array([-1.47298021, -4.10931435,  2.60899782])\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2aaba88f3e10>"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(simple_data[:, 0], simple_data[:, 1], c=simple_data[:, -1])\n",
    "# plt.ylim(-5, 2)\n",
    "# plt.xlim(-5, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [],
   "source": [
    "# num= 50\n",
    "# x = np.linspace(-12, 2, num=num)\n",
    "# y = np.linspace(-12, 2, num=num)\n",
    "# X,Y = np.meshgrid(x, y) # grid of point\n",
    "# Z = gp.predict(np.stack((X.ravel(), Y.ravel()), axis=1)).reshape(*[num]*2) # evaluation of the function on the grid\n",
    "\n",
    "# im = plt.imshow(np.log10(Z), cmap='viridis', extent=[-12, 2, -12, 2], origin='lower')\n",
    "# plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bayes_opt import BayesianOptimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = BayesianOptimization(\n",
    "    f=bb,\n",
    "    pbounds=pbounds,\n",
    "    random_state=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   iter    |  target   |    s0     |    s1     |    s2     |    s3     |\n",
      "-------------------------------------------------------------------------\n",
      "| \u001b[0m 1       \u001b[0m | \u001b[0m-19.02   \u001b[0m | \u001b[0m-2.489   \u001b[0m | \u001b[0m 6.61    \u001b[0m | \u001b[0m-15.0    \u001b[0m | \u001b[0m-5.93    \u001b[0m |\n",
      "| \u001b[0m 2       \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m-10.6    \u001b[0m | \u001b[0m-12.23   \u001b[0m | \u001b[0m-9.412   \u001b[0m | \u001b[0m-4.633   \u001b[0m |\n",
      "| \u001b[95m 3       \u001b[0m | \u001b[95m-12.14   \u001b[0m | \u001b[95m-3.097   \u001b[0m | \u001b[95m 1.165   \u001b[0m | \u001b[95m-2.424   \u001b[0m | \u001b[95m 5.557   \u001b[0m |\n",
      "| \u001b[0m 4       \u001b[0m | \u001b[0m-22.75   \u001b[0m | \u001b[0m-8.866   \u001b[0m | \u001b[0m 11.34   \u001b[0m | \u001b[0m-14.18   \u001b[0m | \u001b[0m 5.114   \u001b[0m |\n",
      "| \u001b[0m 5       \u001b[0m | \u001b[0m-18.32   \u001b[0m | \u001b[0m-2.481   \u001b[0m | \u001b[0m 1.761   \u001b[0m | \u001b[0m-10.79   \u001b[0m | \u001b[0m-9.057   \u001b[0m |\n",
      "| \u001b[0m 6       \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 9.022   \u001b[0m | \u001b[0m 14.05   \u001b[0m | \u001b[0m-5.597   \u001b[0m | \u001b[0m 5.77    \u001b[0m |\n",
      "| \u001b[0m 7       \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 11.29   \u001b[0m | \u001b[0m 11.84   \u001b[0m | \u001b[0m-12.45   \u001b[0m | \u001b[0m-13.83   \u001b[0m |\n",
      "| \u001b[0m 8       \u001b[0m | \u001b[0m-23.0    \u001b[0m | \u001b[0m-9.905   \u001b[0m | \u001b[0m 11.34   \u001b[0m | \u001b[0m-12.05   \u001b[0m | \u001b[0m-2.367   \u001b[0m |\n",
      "| \u001b[0m 9       \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 13.74   \u001b[0m | \u001b[0m 0.995   \u001b[0m | \u001b[0m 5.756   \u001b[0m | \u001b[0m-5.535   \u001b[0m |\n",
      "| \u001b[0m 10      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 5.595   \u001b[0m | \u001b[0m 10.04   \u001b[0m | \u001b[0m-14.45   \u001b[0m | \u001b[0m 7.504   \u001b[0m |\n",
      "| \u001b[0m 11      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 14.67   \u001b[0m | \u001b[0m 7.445   \u001b[0m | \u001b[0m-6.587   \u001b[0m | \u001b[0m 8.678   \u001b[0m |\n",
      "| \u001b[0m 12      \u001b[0m | \u001b[0m-25.32   \u001b[0m | \u001b[0m-11.9    \u001b[0m | \u001b[0m-1.563   \u001b[0m | \u001b[0m 12.26   \u001b[0m | \u001b[0m-6.192   \u001b[0m |\n",
      "| \u001b[0m 13      \u001b[0m | \u001b[0m-20.77   \u001b[0m | \u001b[0m-6.367   \u001b[0m | \u001b[0m-11.1    \u001b[0m | \u001b[0m-14.42   \u001b[0m | \u001b[0m 5.365   \u001b[0m |\n",
      "| \u001b[0m 14      \u001b[0m | \u001b[0m-23.7    \u001b[0m | \u001b[0m-8.651   \u001b[0m | \u001b[0m-7.034   \u001b[0m | \u001b[0m-0.2528  \u001b[0m | \u001b[0m-13.4    \u001b[0m |\n",
      "| \u001b[0m 15      \u001b[0m | \u001b[0m-18.32   \u001b[0m | \u001b[0m 2.224   \u001b[0m | \u001b[0m-10.6    \u001b[0m | \u001b[0m 2.679   \u001b[0m | \u001b[0m 5.993   \u001b[0m |\n",
      "| \u001b[0m 16      \u001b[0m | \u001b[0m-24.88   \u001b[0m | \u001b[0m-11.93   \u001b[0m | \u001b[0m-2.578   \u001b[0m | \u001b[0m 5.832   \u001b[0m | \u001b[0m-2.575   \u001b[0m |\n",
      "| \u001b[0m 17      \u001b[0m | \u001b[0m-27.36   \u001b[0m | \u001b[0m-13.5    \u001b[0m | \u001b[0m 1.077   \u001b[0m | \u001b[0m 4.914   \u001b[0m | \u001b[0m 0.4467  \u001b[0m |\n",
      "| \u001b[0m 18      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 13.34   \u001b[0m | \u001b[0m 2.597   \u001b[0m | \u001b[0m 12.1    \u001b[0m | \u001b[0m-10.88   \u001b[0m |\n",
      "| \u001b[0m 19      \u001b[0m | \u001b[0m-23.24   \u001b[0m | \u001b[0m-10.82   \u001b[0m | \u001b[0m 9.222   \u001b[0m | \u001b[0m-3.07    \u001b[0m | \u001b[0m-10.04   \u001b[0m |\n",
      "| \u001b[0m 20      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 12.83   \u001b[0m | \u001b[0m-4.567   \u001b[0m | \u001b[0m 7.524   \u001b[0m | \u001b[0m 6.78    \u001b[0m |\n",
      "| \u001b[0m 21      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 11.5    \u001b[0m | \u001b[0m 3.71    \u001b[0m | \u001b[0m 7.528   \u001b[0m | \u001b[0m-4.533   \u001b[0m |\n",
      "| \u001b[0m 22      \u001b[0m | \u001b[0m-15.84   \u001b[0m | \u001b[0m-6.902   \u001b[0m | \u001b[0m 11.88   \u001b[0m | \u001b[0m-2.157   \u001b[0m | \u001b[0m 13.95   \u001b[0m |\n",
      "| \u001b[0m 23      \u001b[0m | \u001b[0m-17.55   \u001b[0m | \u001b[0m 4.903   \u001b[0m | \u001b[0m 3.651   \u001b[0m | \u001b[0m-11.56   \u001b[0m | \u001b[0m 13.48   \u001b[0m |\n",
      "| \u001b[0m 24      \u001b[0m | \u001b[0m-16.76   \u001b[0m | \u001b[0m-1.503   \u001b[0m | \u001b[0m 2.352   \u001b[0m | \u001b[0m-2.756   \u001b[0m | \u001b[0m-7.889   \u001b[0m |\n",
      "| \u001b[0m 25      \u001b[0m | \u001b[0m-22.98   \u001b[0m | \u001b[0m 12.1    \u001b[0m | \u001b[0m 2.21    \u001b[0m | \u001b[0m-14.91   \u001b[0m | \u001b[0m 3.514   \u001b[0m |\n",
      "| \u001b[0m 26      \u001b[0m | \u001b[0m-13.04   \u001b[0m | \u001b[0m-5.201   \u001b[0m | \u001b[0m 0.8117  \u001b[0m | \u001b[0m 11.58   \u001b[0m | \u001b[0m-4.282   \u001b[0m |\n",
      "| \u001b[0m 27      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 12.26   \u001b[0m | \u001b[0m 3.701   \u001b[0m | \u001b[0m-14.53   \u001b[0m | \u001b[0m 12.88   \u001b[0m |\n",
      "| \u001b[0m 28      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 5.727   \u001b[0m | \u001b[0m 14.92   \u001b[0m | \u001b[0m-9.83    \u001b[0m | \u001b[0m-10.89   \u001b[0m |\n",
      "| \u001b[0m 29      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 12.98   \u001b[0m | \u001b[0m 5.905   \u001b[0m | \u001b[0m-13.02   \u001b[0m | \u001b[0m 7.664   \u001b[0m |\n",
      "| \u001b[0m 30      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 7.616   \u001b[0m | \u001b[0m 12.69   \u001b[0m | \u001b[0m 6.346   \u001b[0m | \u001b[0m-11.27   \u001b[0m |\n",
      "| \u001b[0m 31      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m-14.4    \u001b[0m | \u001b[0m-14.21   \u001b[0m | \u001b[0m-14.15   \u001b[0m | \u001b[0m-7.614   \u001b[0m |\n",
      "| \u001b[0m 32      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 10.8    \u001b[0m | \u001b[0m 1.165   \u001b[0m | \u001b[0m 1.585   \u001b[0m | \u001b[0m 10.26   \u001b[0m |\n",
      "| \u001b[0m 33      \u001b[0m | \u001b[0m-23.87   \u001b[0m | \u001b[0m-11.27   \u001b[0m | \u001b[0m-6.624   \u001b[0m | \u001b[0m 2.573   \u001b[0m | \u001b[0m 14.09   \u001b[0m |\n",
      "| \u001b[0m 34      \u001b[0m | \u001b[0m-20.67   \u001b[0m | \u001b[0m 1.831   \u001b[0m | \u001b[0m-14.44   \u001b[0m | \u001b[0m 9.019   \u001b[0m | \u001b[0m-8.011   \u001b[0m |\n",
      "| \u001b[0m 35      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 9.213   \u001b[0m | \u001b[0m-3.364   \u001b[0m | \u001b[0m 10.91   \u001b[0m | \u001b[0m 7.414   \u001b[0m |\n",
      "| \u001b[0m 36      \u001b[0m | \u001b[0m-19.49   \u001b[0m | \u001b[0m 1.687   \u001b[0m | \u001b[0m-10.91   \u001b[0m | \u001b[0m-13.2    \u001b[0m | \u001b[0m-11.36   \u001b[0m |\n",
      "| \u001b[0m 37      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m-13.66   \u001b[0m | \u001b[0m-11.78   \u001b[0m | \u001b[0m-8.229   \u001b[0m | \u001b[0m 6.39    \u001b[0m |\n",
      "| \u001b[0m 38      \u001b[0m | \u001b[0m-20.98   \u001b[0m | \u001b[0m 1.792   \u001b[0m | \u001b[0m-14.62   \u001b[0m | \u001b[0m-12.84   \u001b[0m | \u001b[0m 14.02   \u001b[0m |\n",
      "| \u001b[0m 39      \u001b[0m | \u001b[0m-16.78   \u001b[0m | \u001b[0m 2.043   \u001b[0m | \u001b[0m-8.901   \u001b[0m | \u001b[0m-7.43    \u001b[0m | \u001b[0m 7.315   \u001b[0m |\n",
      "| \u001b[0m 40      \u001b[0m | \u001b[0m-19.95   \u001b[0m | \u001b[0m-9.137   \u001b[0m | \u001b[0m 2.441   \u001b[0m | \u001b[0m 14.1    \u001b[0m | \u001b[0m 10.4    \u001b[0m |\n",
      "| \u001b[0m 41      \u001b[0m | \u001b[0m-17.6    \u001b[0m | \u001b[0m-7.805   \u001b[0m | \u001b[0m-0.1869  \u001b[0m | \u001b[0m 3.599   \u001b[0m | \u001b[0m 9.869   \u001b[0m |\n",
      "| \u001b[0m 42      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m-10.3    \u001b[0m | \u001b[0m-14.44   \u001b[0m | \u001b[0m-12.9    \u001b[0m | \u001b[0m-0.4096  \u001b[0m |\n",
      "| \u001b[0m 43      \u001b[0m | \u001b[0m-15.0    \u001b[0m | \u001b[0m 3.19    \u001b[0m | \u001b[0m 2.066   \u001b[0m | \u001b[0m-5.479   \u001b[0m | \u001b[0m 14.66   \u001b[0m |\n",
      "| \u001b[95m 44      \u001b[0m | \u001b[95m-11.6    \u001b[0m | \u001b[95m 2.392   \u001b[0m | \u001b[95m-3.596   \u001b[0m | \u001b[95m 1.528   \u001b[0m | \u001b[95m 7.36    \u001b[0m |\n",
      "| \u001b[0m 45      \u001b[0m | \u001b[0m-19.99   \u001b[0m | \u001b[0m 5.077   \u001b[0m | \u001b[0m-7.052   \u001b[0m | \u001b[0m-13.01   \u001b[0m | \u001b[0m-3.897   \u001b[0m |\n",
      "| \u001b[0m 46      \u001b[0m | \u001b[0m-22.57   \u001b[0m | \u001b[0m 3.892   \u001b[0m | \u001b[0m-8.695   \u001b[0m | \u001b[0m 7.583   \u001b[0m | \u001b[0m-13.0    \u001b[0m |\n",
      "| \u001b[0m 47      \u001b[0m | \u001b[0m-17.34   \u001b[0m | \u001b[0m-7.191   \u001b[0m | \u001b[0m 9.143   \u001b[0m | \u001b[0m-9.197   \u001b[0m | \u001b[0m 4.184   \u001b[0m |\n",
      "| \u001b[0m 48      \u001b[0m | \u001b[0m-21.49   \u001b[0m | \u001b[0m 0.7401  \u001b[0m | \u001b[0m 12.74   \u001b[0m | \u001b[0m-7.101   \u001b[0m | \u001b[0m-13.02   \u001b[0m |\n",
      "| \u001b[0m 49      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 7.052   \u001b[0m | \u001b[0m 8.165   \u001b[0m | \u001b[0m 12.23   \u001b[0m | \u001b[0m 12.96   \u001b[0m |\n",
      "| \u001b[0m 50      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m-14.58   \u001b[0m | \u001b[0m-7.969   \u001b[0m | \u001b[0m 3.503   \u001b[0m | \u001b[0m 13.47   \u001b[0m |\n",
      "| \u001b[0m 51      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 13.51   \u001b[0m | \u001b[0m 1.7     \u001b[0m | \u001b[0m 12.47   \u001b[0m | \u001b[0m 4.247   \u001b[0m |\n",
      "| \u001b[0m 52      \u001b[0m | \u001b[0m-11.69   \u001b[0m | \u001b[0m-3.3     \u001b[0m | \u001b[0m-0.4203  \u001b[0m | \u001b[0m 3.129   \u001b[0m | \u001b[0m 1.486   \u001b[0m |\n",
      "| \u001b[0m 53      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 12.79   \u001b[0m | \u001b[0m 12.56   \u001b[0m | \u001b[0m-3.154   \u001b[0m | \u001b[0m 13.9    \u001b[0m |\n",
      "| \u001b[0m 54      \u001b[0m | \u001b[0m-21.85   \u001b[0m | \u001b[0m-9.781   \u001b[0m | \u001b[0m-11.21   \u001b[0m | \u001b[0m-10.95   \u001b[0m | \u001b[0m 0.1699  \u001b[0m |\n",
      "| \u001b[0m 55      \u001b[0m | \u001b[0m-29.63   \u001b[0m | \u001b[0m-14.35   \u001b[0m | \u001b[0m 13.44   \u001b[0m | \u001b[0m 9.813   \u001b[0m | \u001b[0m-14.55   \u001b[0m |\n",
      "| \u001b[0m 56      \u001b[0m | \u001b[0m-21.06   \u001b[0m | \u001b[0m-9.714   \u001b[0m | \u001b[0m-5.038   \u001b[0m | \u001b[0m-11.07   \u001b[0m | \u001b[0m 9.285   \u001b[0m |\n",
      "| \u001b[0m 57      \u001b[0m | \u001b[0m-12.6    \u001b[0m | \u001b[0m-4.658   \u001b[0m | \u001b[0m 13.2    \u001b[0m | \u001b[0m 2.46    \u001b[0m | \u001b[0m 11.36   \u001b[0m |\n",
      "| \u001b[0m 58      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 10.34   \u001b[0m | \u001b[0m 12.16   \u001b[0m | \u001b[0m-1.204   \u001b[0m | \u001b[0m 1.39    \u001b[0m |\n",
      "| \u001b[0m 59      \u001b[0m | \u001b[0m-26.71   \u001b[0m | \u001b[0m 8.958   \u001b[0m | \u001b[0m-6.428   \u001b[0m | \u001b[0m-0.2924  \u001b[0m | \u001b[0m 2.973   \u001b[0m |\n",
      "| \u001b[0m 60      \u001b[0m | \u001b[0m-28.34   \u001b[0m | \u001b[0m-14.53   \u001b[0m | \u001b[0m 2.804   \u001b[0m | \u001b[0m-1.99    \u001b[0m | \u001b[0m 9.221   \u001b[0m |\n",
      "| \u001b[0m 61      \u001b[0m | \u001b[0m-17.61   \u001b[0m | \u001b[0m-5.543   \u001b[0m | \u001b[0m 11.79   \u001b[0m | \u001b[0m 2.336   \u001b[0m | \u001b[0m-9.48    \u001b[0m |\n",
      "| \u001b[0m 62      \u001b[0m | \u001b[0m-21.1    \u001b[0m | \u001b[0m 8.638   \u001b[0m | \u001b[0m 3.361   \u001b[0m | \u001b[0m-13.38   \u001b[0m | \u001b[0m-2.394   \u001b[0m |\n",
      "| \u001b[0m 63      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 5.372   \u001b[0m | \u001b[0m 12.56   \u001b[0m | \u001b[0m-14.99   \u001b[0m | \u001b[0m 14.3    \u001b[0m |\n",
      "| \u001b[95m 64      \u001b[0m | \u001b[95m-11.49   \u001b[0m | \u001b[95m-3.703   \u001b[0m | \u001b[95m 14.21   \u001b[0m | \u001b[95m 3.141   \u001b[0m | \u001b[95m 9.865   \u001b[0m |\n",
      "| \u001b[0m 65      \u001b[0m | \u001b[0m-14.13   \u001b[0m | \u001b[0m 2.241   \u001b[0m | \u001b[0m 3.842   \u001b[0m | \u001b[0m-6.433   \u001b[0m | \u001b[0m 2.605   \u001b[0m |\n",
      "| \u001b[0m 66      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 7.501   \u001b[0m | \u001b[0m 10.75   \u001b[0m | \u001b[0m 7.652   \u001b[0m | \u001b[0m 5.942   \u001b[0m |\n",
      "| \u001b[0m 67      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 10.93   \u001b[0m | \u001b[0m-5.32    \u001b[0m | \u001b[0m 5.124   \u001b[0m | \u001b[0m-1.474   \u001b[0m |\n",
      "| \u001b[0m 68      \u001b[0m | \u001b[0m-12.63   \u001b[0m | \u001b[0m-3.537   \u001b[0m | \u001b[0m-2.676   \u001b[0m | \u001b[0m-2.956   \u001b[0m | \u001b[0m-5.478   \u001b[0m |\n",
      "| \u001b[0m 69      \u001b[0m | \u001b[0m-13.19   \u001b[0m | \u001b[0m 3.658   \u001b[0m | \u001b[0m-2.093   \u001b[0m | \u001b[0m 14.21   \u001b[0m | \u001b[0m 5.334   \u001b[0m |\n",
      "| \u001b[0m 70      \u001b[0m | \u001b[0m-20.07   \u001b[0m | \u001b[0m-9.043   \u001b[0m | \u001b[0m-2.199   \u001b[0m | \u001b[0m-4.7     \u001b[0m | \u001b[0m 8.929   \u001b[0m |\n",
      "| \u001b[0m 71      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 11.4    \u001b[0m | \u001b[0m 12.12   \u001b[0m | \u001b[0m 4.882   \u001b[0m | \u001b[0m-6.894   \u001b[0m |\n",
      "| \u001b[0m 72      \u001b[0m | \u001b[0m-16.88   \u001b[0m | \u001b[0m-7.429   \u001b[0m | \u001b[0m 10.65   \u001b[0m | \u001b[0m 0.8314  \u001b[0m | \u001b[0m 9.065   \u001b[0m |\n",
      "| \u001b[95m 73      \u001b[0m | \u001b[95m-9.559   \u001b[0m | \u001b[95m 2.175   \u001b[0m | \u001b[95m 6.994   \u001b[0m | \u001b[95m 0.5703  \u001b[0m | \u001b[95m 8.127   \u001b[0m |\n",
      "| \u001b[0m 74      \u001b[0m | \u001b[0m-20.15   \u001b[0m | \u001b[0m 2.066   \u001b[0m | \u001b[0m-1.029   \u001b[0m | \u001b[0m-4.719   \u001b[0m | \u001b[0m-12.95   \u001b[0m |\n",
      "| \u001b[0m 75      \u001b[0m | \u001b[0m-17.03   \u001b[0m | \u001b[0m-3.662   \u001b[0m | \u001b[0m-12.61   \u001b[0m | \u001b[0m 14.48   \u001b[0m | \u001b[0m-9.552   \u001b[0m |\n",
      "| \u001b[0m 76      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 9.356   \u001b[0m | \u001b[0m 11.25   \u001b[0m | \u001b[0m 5.652   \u001b[0m | \u001b[0m 2.085   \u001b[0m |\n",
      "| \u001b[0m 77      \u001b[0m | \u001b[0m-21.64   \u001b[0m | \u001b[0m-10.17   \u001b[0m | \u001b[0m-0.9936  \u001b[0m | \u001b[0m-4.645   \u001b[0m | \u001b[0m-8.249   \u001b[0m |\n",
      "| \u001b[0m 78      \u001b[0m | \u001b[0m-12.7    \u001b[0m | \u001b[0m 2.775   \u001b[0m | \u001b[0m-5.632   \u001b[0m | \u001b[0m 12.49   \u001b[0m | \u001b[0m 12.29   \u001b[0m |\n",
      "| \u001b[0m 79      \u001b[0m | \u001b[0m-19.55   \u001b[0m | \u001b[0m-7.286   \u001b[0m | \u001b[0m-11.67   \u001b[0m | \u001b[0m-9.211   \u001b[0m | \u001b[0m-0.01247 \u001b[0m |\n",
      "| \u001b[0m 80      \u001b[0m | \u001b[0m-15.86   \u001b[0m | \u001b[0m 6.858   \u001b[0m | \u001b[0m-8.754   \u001b[0m | \u001b[0m-7.559   \u001b[0m | \u001b[0m 10.55   \u001b[0m |\n",
      "| \u001b[0m 81      \u001b[0m | \u001b[0m-19.32   \u001b[0m | \u001b[0m-2.525   \u001b[0m | \u001b[0m 3.501   \u001b[0m | \u001b[0m-7.99    \u001b[0m | \u001b[0m-11.94   \u001b[0m |\n",
      "| \u001b[0m 82      \u001b[0m | \u001b[0m-17.1    \u001b[0m | \u001b[0m 0.4757  \u001b[0m | \u001b[0m-0.6858  \u001b[0m | \u001b[0m-10.42   \u001b[0m | \u001b[0m 3.654   \u001b[0m |\n",
      "| \u001b[0m 83      \u001b[0m | \u001b[0m-16.93   \u001b[0m | \u001b[0m 1.32    \u001b[0m | \u001b[0m 4.624   \u001b[0m | \u001b[0m-10.66   \u001b[0m | \u001b[0m 7.546   \u001b[0m |\n",
      "| \u001b[0m 84      \u001b[0m | \u001b[0m-23.66   \u001b[0m | \u001b[0m-8.339   \u001b[0m | \u001b[0m 0.5806  \u001b[0m | \u001b[0m 8.559   \u001b[0m | \u001b[0m-14.33   \u001b[0m |\n",
      "| \u001b[0m 85      \u001b[0m | \u001b[0m-13.61   \u001b[0m | \u001b[0m-5.269   \u001b[0m | \u001b[0m 11.19   \u001b[0m | \u001b[0m 10.34   \u001b[0m | \u001b[0m 1.153   \u001b[0m |\n",
      "| \u001b[0m 86      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 11.0    \u001b[0m | \u001b[0m 13.49   \u001b[0m | \u001b[0m 9.792   \u001b[0m | \u001b[0m 10.62   \u001b[0m |\n",
      "| \u001b[0m 87      \u001b[0m | \u001b[0m-24.91   \u001b[0m | \u001b[0m-12.04   \u001b[0m | \u001b[0m 4.539   \u001b[0m | \u001b[0m 6.106   \u001b[0m | \u001b[0m 3.307   \u001b[0m |\n",
      "| \u001b[0m 88      \u001b[0m | \u001b[0m-22.61   \u001b[0m | \u001b[0m 8.988   \u001b[0m | \u001b[0m-13.96   \u001b[0m | \u001b[0m 8.107   \u001b[0m | \u001b[0m 6.952   \u001b[0m |\n",
      "| \u001b[0m 89      \u001b[0m | \u001b[0m-16.9    \u001b[0m | \u001b[0m-7.209   \u001b[0m | \u001b[0m-7.288   \u001b[0m | \u001b[0m 3.969   \u001b[0m | \u001b[0m-4.641   \u001b[0m |\n",
      "| \u001b[0m 90      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 8.898   \u001b[0m | \u001b[0m-1.616   \u001b[0m | \u001b[0m 8.482   \u001b[0m | \u001b[0m 14.71   \u001b[0m |\n",
      "| \u001b[0m 91      \u001b[0m | \u001b[0m-17.63   \u001b[0m | \u001b[0m-5.993   \u001b[0m | \u001b[0m-10.71   \u001b[0m | \u001b[0m 12.04   \u001b[0m | \u001b[0m 1.247   \u001b[0m |\n",
      "| \u001b[0m 92      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 14.24   \u001b[0m | \u001b[0m 4.098   \u001b[0m | \u001b[0m 14.82   \u001b[0m | \u001b[0m 1.382   \u001b[0m |\n",
      "| \u001b[0m 93      \u001b[0m | \u001b[0m-22.17   \u001b[0m | \u001b[0m 0.7928  \u001b[0m | \u001b[0m-10.94   \u001b[0m | \u001b[0m-4.329   \u001b[0m | \u001b[0m-14.21   \u001b[0m |\n",
      "| \u001b[0m 94      \u001b[0m | \u001b[0m-24.24   \u001b[0m | \u001b[0m-10.19   \u001b[0m | \u001b[0m 7.369   \u001b[0m | \u001b[0m-14.09   \u001b[0m | \u001b[0m-4.004   \u001b[0m |\n",
      "| \u001b[0m 95      \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 10.87   \u001b[0m | \u001b[0m 5.78    \u001b[0m | \u001b[0m 5.728   \u001b[0m | \u001b[0m-9.341   \u001b[0m |\n",
      "| \u001b[0m 96      \u001b[0m | \u001b[0m-16.75   \u001b[0m | \u001b[0m-1.743   \u001b[0m | \u001b[0m 2.447   \u001b[0m | \u001b[0m 14.69   \u001b[0m | \u001b[0m-8.883   \u001b[0m |\n",
      "| \u001b[0m 97      \u001b[0m | \u001b[0m-17.57   \u001b[0m | \u001b[0m-7.568   \u001b[0m | \u001b[0m-7.135   \u001b[0m | \u001b[0m 7.505   \u001b[0m | \u001b[0m-1.291   \u001b[0m |\n",
      "| \u001b[0m 98      \u001b[0m | \u001b[0m-27.31   \u001b[0m | \u001b[0m-13.29   \u001b[0m | \u001b[0m 0.2555  \u001b[0m | \u001b[0m-8.641   \u001b[0m | \u001b[0m 8.958   \u001b[0m |\n",
      "| \u001b[0m 99      \u001b[0m | \u001b[0m-21.05   \u001b[0m | \u001b[0m-6.08    \u001b[0m | \u001b[0m-14.17   \u001b[0m | \u001b[0m 2.803   \u001b[0m | \u001b[0m 10.32   \u001b[0m |\n",
      "| \u001b[0m 100     \u001b[0m | \u001b[0m-11.36   \u001b[0m | \u001b[0m-3.57    \u001b[0m | \u001b[0m 7.496   \u001b[0m | \u001b[0m 0.3342  \u001b[0m | \u001b[0m 1.229   \u001b[0m |\n",
      "| \u001b[0m 101     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 13.78   \u001b[0m | \u001b[0m 9.119   \u001b[0m | \u001b[0m-14.03   \u001b[0m | \u001b[0m 6.282   \u001b[0m |\n",
      "| \u001b[0m 102     \u001b[0m | \u001b[0m-16.75   \u001b[0m | \u001b[0m-1.05    \u001b[0m | \u001b[0m 13.43   \u001b[0m | \u001b[0m-8.357   \u001b[0m | \u001b[0m-6.988   \u001b[0m |\n",
      "| \u001b[0m 103     \u001b[0m | \u001b[0m-25.76   \u001b[0m | \u001b[0m-12.56   \u001b[0m | \u001b[0m-2.141   \u001b[0m | \u001b[0m-11.73   \u001b[0m | \u001b[0m 4.014   \u001b[0m |\n",
      "| \u001b[0m 104     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 9.089   \u001b[0m | \u001b[0m 5.904   \u001b[0m | \u001b[0m 7.986   \u001b[0m | \u001b[0m-4.726   \u001b[0m |\n",
      "| \u001b[0m 105     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 10.38   \u001b[0m | \u001b[0m-2.137   \u001b[0m | \u001b[0m 9.72    \u001b[0m | \u001b[0m 3.795   \u001b[0m |\n",
      "| \u001b[0m 106     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m-10.7    \u001b[0m | \u001b[0m-12.65   \u001b[0m | \u001b[0m-14.45   \u001b[0m | \u001b[0m-13.0    \u001b[0m |\n",
      "| \u001b[0m 107     \u001b[0m | \u001b[0m-17.86   \u001b[0m | \u001b[0m-1.242   \u001b[0m | \u001b[0m-11.6    \u001b[0m | \u001b[0m-14.17   \u001b[0m | \u001b[0m 7.646   \u001b[0m |\n",
      "| \u001b[0m 108     \u001b[0m | \u001b[0m-11.85   \u001b[0m | \u001b[0m-3.154   \u001b[0m | \u001b[0m 7.408   \u001b[0m | \u001b[0m-1.428   \u001b[0m | \u001b[0m-1.497   \u001b[0m |\n",
      "| \u001b[0m 109     \u001b[0m | \u001b[0m-11.69   \u001b[0m | \u001b[0m-0.6578  \u001b[0m | \u001b[0m-0.7799  \u001b[0m | \u001b[0m 9.095   \u001b[0m | \u001b[0m-2.928   \u001b[0m |\n",
      "| \u001b[0m 110     \u001b[0m | \u001b[0m-21.02   \u001b[0m | \u001b[0m 12.14   \u001b[0m | \u001b[0m-13.89   \u001b[0m | \u001b[0m 8.216   \u001b[0m | \u001b[0m-11.23   \u001b[0m |\n",
      "| \u001b[0m 111     \u001b[0m | \u001b[0m-26.34   \u001b[0m | \u001b[0m 3.555   \u001b[0m | \u001b[0m-14.69   \u001b[0m | \u001b[0m 1.159   \u001b[0m | \u001b[0m-14.91   \u001b[0m |\n",
      "| \u001b[0m 112     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 13.54   \u001b[0m | \u001b[0m 12.16   \u001b[0m | \u001b[0m 8.879   \u001b[0m | \u001b[0m 12.46   \u001b[0m |\n",
      "| \u001b[0m 113     \u001b[0m | \u001b[0m-22.65   \u001b[0m | \u001b[0m-10.63   \u001b[0m | \u001b[0m-10.27   \u001b[0m | \u001b[0m-9.371   \u001b[0m | \u001b[0m 3.675   \u001b[0m |\n",
      "| \u001b[0m 114     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 12.17   \u001b[0m | \u001b[0m 14.7    \u001b[0m | \u001b[0m 6.334   \u001b[0m | \u001b[0m 6.954   \u001b[0m |\n",
      "| \u001b[0m 115     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 12.28   \u001b[0m | \u001b[0m-2.974   \u001b[0m | \u001b[0m-7.504   \u001b[0m | \u001b[0m-9.797   \u001b[0m |\n",
      "| \u001b[0m 116     \u001b[0m | \u001b[0m-23.19   \u001b[0m | \u001b[0m-11.42   \u001b[0m | \u001b[0m 9.378   \u001b[0m | \u001b[0m-10.6    \u001b[0m | \u001b[0m-7.071   \u001b[0m |\n",
      "| \u001b[0m 117     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 9.573   \u001b[0m | \u001b[0m-5.682   \u001b[0m | \u001b[0m 14.47   \u001b[0m | \u001b[0m-7.001   \u001b[0m |\n",
      "| \u001b[0m 118     \u001b[0m | \u001b[0m-12.86   \u001b[0m | \u001b[0m 1.01    \u001b[0m | \u001b[0m-5.566   \u001b[0m | \u001b[0m 12.32   \u001b[0m | \u001b[0m-4.003   \u001b[0m |\n",
      "| \u001b[0m 119     \u001b[0m | \u001b[0m-22.85   \u001b[0m | \u001b[0m-1.992   \u001b[0m | \u001b[0m 0.3688  \u001b[0m | \u001b[0m 13.17   \u001b[0m | \u001b[0m-14.07   \u001b[0m |\n",
      "| \u001b[0m 120     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 6.506   \u001b[0m | \u001b[0m 11.73   \u001b[0m | \u001b[0m-14.18   \u001b[0m | \u001b[0m 0.6615  \u001b[0m |\n",
      "| \u001b[0m 121     \u001b[0m | \u001b[0m-12.93   \u001b[0m | \u001b[0m-5.22    \u001b[0m | \u001b[0m 10.78   \u001b[0m | \u001b[0m 1.755   \u001b[0m | \u001b[0m 5.707   \u001b[0m |\n",
      "| \u001b[0m 122     \u001b[0m | \u001b[0m-22.22   \u001b[0m | \u001b[0m-1.414   \u001b[0m | \u001b[0m 3.849   \u001b[0m | \u001b[0m-6.297   \u001b[0m | \u001b[0m-14.72   \u001b[0m |\n",
      "| \u001b[0m 123     \u001b[0m | \u001b[0m-13.34   \u001b[0m | \u001b[0m 2.303   \u001b[0m | \u001b[0m-5.657   \u001b[0m | \u001b[0m 0.518   \u001b[0m | \u001b[0m 12.49   \u001b[0m |\n",
      "| \u001b[0m 124     \u001b[0m | \u001b[0m-11.38   \u001b[0m | \u001b[0m-2.206   \u001b[0m | \u001b[0m-7.578   \u001b[0m | \u001b[0m-3.861   \u001b[0m | \u001b[0m 12.96   \u001b[0m |\n",
      "| \u001b[0m 125     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 13.11   \u001b[0m | \u001b[0m 10.33   \u001b[0m | \u001b[0m 12.61   \u001b[0m | \u001b[0m-8.163   \u001b[0m |\n",
      "| \u001b[0m 126     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m-12.38   \u001b[0m | \u001b[0m-8.181   \u001b[0m | \u001b[0m-5.569   \u001b[0m | \u001b[0m-9.757   \u001b[0m |\n",
      "| \u001b[0m 127     \u001b[0m | \u001b[0m-17.73   \u001b[0m | \u001b[0m 3.213   \u001b[0m | \u001b[0m-2.592   \u001b[0m | \u001b[0m 9.491   \u001b[0m | \u001b[0m-9.446   \u001b[0m |\n",
      "| \u001b[0m 128     \u001b[0m | \u001b[0m-16.57   \u001b[0m | \u001b[0m 6.056   \u001b[0m | \u001b[0m-7.789   \u001b[0m | \u001b[0m 2.227   \u001b[0m | \u001b[0m-4.53    \u001b[0m |\n",
      "| \u001b[0m 129     \u001b[0m | \u001b[0m-27.6    \u001b[0m | \u001b[0m-13.29   \u001b[0m | \u001b[0m-8.136   \u001b[0m | \u001b[0m 4.923   \u001b[0m | \u001b[0m-0.0825  \u001b[0m |\n",
      "| \u001b[0m 130     \u001b[0m | \u001b[0m-16.63   \u001b[0m | \u001b[0m 0.5705  \u001b[0m | \u001b[0m-9.758   \u001b[0m | \u001b[0m 2.121   \u001b[0m | \u001b[0m 14.9    \u001b[0m |\n",
      "| \u001b[0m 131     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 9.505   \u001b[0m | \u001b[0m 2.831   \u001b[0m | \u001b[0m 14.28   \u001b[0m | \u001b[0m 12.05   \u001b[0m |\n",
      "| \u001b[0m 132     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 2.868   \u001b[0m | \u001b[0m-14.03   \u001b[0m | \u001b[0m-12.19   \u001b[0m | \u001b[0m-13.04   \u001b[0m |\n",
      "| \u001b[0m 133     \u001b[0m | \u001b[0m-15.16   \u001b[0m | \u001b[0m-1.448   \u001b[0m | \u001b[0m-3.737   \u001b[0m | \u001b[0m 14.26   \u001b[0m | \u001b[0m-9.961   \u001b[0m |\n",
      "| \u001b[0m 134     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 14.18   \u001b[0m | \u001b[0m 8.024   \u001b[0m | \u001b[0m 9.727   \u001b[0m | \u001b[0m 3.978   \u001b[0m |\n",
      "| \u001b[0m 135     \u001b[0m | \u001b[0m-24.85   \u001b[0m | \u001b[0m 5.062   \u001b[0m | \u001b[0m-0.6935  \u001b[0m | \u001b[0m-14.61   \u001b[0m | \u001b[0m-4.41    \u001b[0m |\n",
      "| \u001b[0m 136     \u001b[0m | \u001b[0m-10.67   \u001b[0m | \u001b[0m-0.2378  \u001b[0m | \u001b[0m 6.903   \u001b[0m | \u001b[0m-0.9411  \u001b[0m | \u001b[0m-1.278   \u001b[0m |\n",
      "| \u001b[0m 137     \u001b[0m | \u001b[0m-25.51   \u001b[0m | \u001b[0m-10.87   \u001b[0m | \u001b[0m-14.67   \u001b[0m | \u001b[0m 7.748   \u001b[0m | \u001b[0m-5.401   \u001b[0m |\n",
      "| \u001b[0m 138     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 14.53   \u001b[0m | \u001b[0m-8.393   \u001b[0m | \u001b[0m-4.839   \u001b[0m | \u001b[0m 0.7169  \u001b[0m |\n",
      "| \u001b[0m 139     \u001b[0m | \u001b[0m-19.2    \u001b[0m | \u001b[0m 7.647   \u001b[0m | \u001b[0m-1.084   \u001b[0m | \u001b[0m-11.26   \u001b[0m | \u001b[0m-5.625   \u001b[0m |\n",
      "| \u001b[0m 140     \u001b[0m | \u001b[0m-19.74   \u001b[0m | \u001b[0m 0.1356  \u001b[0m | \u001b[0m 5.215   \u001b[0m | \u001b[0m 8.104   \u001b[0m | \u001b[0m-11.09   \u001b[0m |\n",
      "| \u001b[0m 141     \u001b[0m | \u001b[0m-29.14   \u001b[0m | \u001b[0m-14.31   \u001b[0m | \u001b[0m 0.5725  \u001b[0m | \u001b[0m 9.3     \u001b[0m | \u001b[0m-14.62   \u001b[0m |\n",
      "| \u001b[0m 142     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 5.174   \u001b[0m | \u001b[0m 5.604   \u001b[0m | \u001b[0m-1.523   \u001b[0m | \u001b[0m 12.44   \u001b[0m |\n",
      "| \u001b[0m 143     \u001b[0m | \u001b[0m-25.49   \u001b[0m | \u001b[0m 4.331   \u001b[0m | \u001b[0m-14.84   \u001b[0m | \u001b[0m-0.4671  \u001b[0m | \u001b[0m 10.78   \u001b[0m |\n",
      "| \u001b[0m 144     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 9.912   \u001b[0m | \u001b[0m 4.475   \u001b[0m | \u001b[0m 5.211   \u001b[0m | \u001b[0m 2.355   \u001b[0m |\n",
      "| \u001b[0m 145     \u001b[0m | \u001b[0m-15.38   \u001b[0m | \u001b[0m-6.776   \u001b[0m | \u001b[0m 1.816   \u001b[0m | \u001b[0m 5.152   \u001b[0m | \u001b[0m-4.427   \u001b[0m |\n",
      "| \u001b[0m 146     \u001b[0m | \u001b[0m-20.77   \u001b[0m | \u001b[0m 10.67   \u001b[0m | \u001b[0m-9.149   \u001b[0m | \u001b[0m 7.42    \u001b[0m | \u001b[0m-6.312   \u001b[0m |\n",
      "| \u001b[0m 147     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 8.214   \u001b[0m | \u001b[0m-2.168   \u001b[0m | \u001b[0m 9.231   \u001b[0m | \u001b[0m-4.394   \u001b[0m |\n",
      "| \u001b[0m 148     \u001b[0m | \u001b[0m-18.59   \u001b[0m | \u001b[0m-8.589   \u001b[0m | \u001b[0m 8.019   \u001b[0m | \u001b[0m-5.741   \u001b[0m | \u001b[0m 6.997   \u001b[0m |\n",
      "| \u001b[0m 149     \u001b[0m | \u001b[0m-17.97   \u001b[0m | \u001b[0m 7.334   \u001b[0m | \u001b[0m-8.358   \u001b[0m | \u001b[0m-8.577   \u001b[0m | \u001b[0m-9.032   \u001b[0m |\n",
      "| \u001b[0m 150     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m-10.72   \u001b[0m | \u001b[0m-3.688   \u001b[0m | \u001b[0m-14.2    \u001b[0m | \u001b[0m-11.67   \u001b[0m |\n",
      "| \u001b[0m 151     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 5.237   \u001b[0m | \u001b[0m 8.993   \u001b[0m | \u001b[0m-12.58   \u001b[0m | \u001b[0m-8.049   \u001b[0m |\n",
      "| \u001b[0m 152     \u001b[0m | \u001b[0m-19.82   \u001b[0m | \u001b[0m-8.771   \u001b[0m | \u001b[0m 12.52   \u001b[0m | \u001b[0m 6.339   \u001b[0m | \u001b[0m 1.617   \u001b[0m |\n",
      "| \u001b[0m 153     \u001b[0m | \u001b[0m-15.21   \u001b[0m | \u001b[0m-5.864   \u001b[0m | \u001b[0m 10.05   \u001b[0m | \u001b[0m-1.941   \u001b[0m | \u001b[0m 12.7    \u001b[0m |\n",
      "| \u001b[0m 154     \u001b[0m | \u001b[0m-19.72   \u001b[0m | \u001b[0m 6.182   \u001b[0m | \u001b[0m-0.6591  \u001b[0m | \u001b[0m-11.21   \u001b[0m | \u001b[0m 14.28   \u001b[0m |\n",
      "| \u001b[0m 155     \u001b[0m | \u001b[0m-21.8    \u001b[0m | \u001b[0m-10.2    \u001b[0m | \u001b[0m-8.922   \u001b[0m | \u001b[0m-2.065   \u001b[0m | \u001b[0m-2.874   \u001b[0m |\n",
      "| \u001b[0m 156     \u001b[0m | \u001b[0m-21.97   \u001b[0m | \u001b[0m-10.6    \u001b[0m | \u001b[0m 6.88    \u001b[0m | \u001b[0m-9.338   \u001b[0m | \u001b[0m 4.317   \u001b[0m |\n",
      "| \u001b[0m 157     \u001b[0m | \u001b[0m-16.47   \u001b[0m | \u001b[0m 7.629   \u001b[0m | \u001b[0m-8.678   \u001b[0m | \u001b[0m 3.029   \u001b[0m | \u001b[0m 7.468   \u001b[0m |\n",
      "| \u001b[0m 158     \u001b[0m | \u001b[0m-20.6    \u001b[0m | \u001b[0m 4.147   \u001b[0m | \u001b[0m 2.914   \u001b[0m | \u001b[0m-6.136   \u001b[0m | \u001b[0m 6.948   \u001b[0m |\n",
      "| \u001b[0m 159     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 13.36   \u001b[0m | \u001b[0m-2.233   \u001b[0m | \u001b[0m 8.465   \u001b[0m | \u001b[0m-13.32   \u001b[0m |\n",
      "| \u001b[0m 160     \u001b[0m | \u001b[0m-17.19   \u001b[0m | \u001b[0m 10.06   \u001b[0m | \u001b[0m-9.232   \u001b[0m | \u001b[0m-3.147   \u001b[0m | \u001b[0m-5.998   \u001b[0m |\n",
      "| \u001b[0m 161     \u001b[0m | \u001b[0m-25.67   \u001b[0m | \u001b[0m-12.6    \u001b[0m | \u001b[0m 12.14   \u001b[0m | \u001b[0m-3.895   \u001b[0m | \u001b[0m 0.9209  \u001b[0m |\n",
      "| \u001b[0m 162     \u001b[0m | \u001b[0m-20.3    \u001b[0m | \u001b[0m-0.1765  \u001b[0m | \u001b[0m-11.04   \u001b[0m | \u001b[0m-8.806   \u001b[0m | \u001b[0m-12.71   \u001b[0m |\n",
      "| \u001b[0m 163     \u001b[0m | \u001b[0m-19.38   \u001b[0m | \u001b[0m 0.2377  \u001b[0m | \u001b[0m-7.154   \u001b[0m | \u001b[0m-4.288   \u001b[0m | \u001b[0m-11.76   \u001b[0m |\n",
      "| \u001b[0m 164     \u001b[0m | \u001b[0m-19.36   \u001b[0m | \u001b[0m 8.627   \u001b[0m | \u001b[0m-11.8    \u001b[0m | \u001b[0m 14.57   \u001b[0m | \u001b[0m-9.685   \u001b[0m |\n",
      "| \u001b[0m 165     \u001b[0m | \u001b[0m-20.41   \u001b[0m | \u001b[0m 2.172   \u001b[0m | \u001b[0m-13.65   \u001b[0m | \u001b[0m 8.613   \u001b[0m | \u001b[0m-9.312   \u001b[0m |\n",
      "| \u001b[0m 166     \u001b[0m | \u001b[0m-17.4    \u001b[0m | \u001b[0m 0.8371  \u001b[0m | \u001b[0m 7.202   \u001b[0m | \u001b[0m-10.5    \u001b[0m | \u001b[0m 1.533   \u001b[0m |\n",
      "| \u001b[0m 167     \u001b[0m | \u001b[0m-20.68   \u001b[0m | \u001b[0m-8.501   \u001b[0m | \u001b[0m 7.776   \u001b[0m | \u001b[0m 6.687   \u001b[0m | \u001b[0m-9.704   \u001b[0m |\n",
      "| \u001b[0m 168     \u001b[0m | \u001b[0m-22.15   \u001b[0m | \u001b[0m 10.86   \u001b[0m | \u001b[0m-14.41   \u001b[0m | \u001b[0m 10.81   \u001b[0m | \u001b[0m 1.767   \u001b[0m |\n",
      "| \u001b[0m 169     \u001b[0m | \u001b[0m-10.13   \u001b[0m | \u001b[0m-2.903   \u001b[0m | \u001b[0m 7.762   \u001b[0m | \u001b[0m 6.508   \u001b[0m | \u001b[0m 14.62   \u001b[0m |\n",
      "| \u001b[0m 170     \u001b[0m | \u001b[0m-21.44   \u001b[0m | \u001b[0m-6.657   \u001b[0m | \u001b[0m-14.89   \u001b[0m | \u001b[0m 13.02   \u001b[0m | \u001b[0m 10.74   \u001b[0m |\n",
      "| \u001b[0m 171     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 6.866   \u001b[0m | \u001b[0m 0.5007  \u001b[0m | \u001b[0m 6.209   \u001b[0m | \u001b[0m 8.416   \u001b[0m |\n",
      "| \u001b[0m 172     \u001b[0m | \u001b[0m-11.85   \u001b[0m | \u001b[0m-3.754   \u001b[0m | \u001b[0m 8.11    \u001b[0m | \u001b[0m 7.519   \u001b[0m | \u001b[0m 3.396   \u001b[0m |\n",
      "| \u001b[0m 173     \u001b[0m | \u001b[0m-19.53   \u001b[0m | \u001b[0m-2.944   \u001b[0m | \u001b[0m 5.919   \u001b[0m | \u001b[0m-14.91   \u001b[0m | \u001b[0m 8.247   \u001b[0m |\n",
      "| \u001b[0m 174     \u001b[0m | \u001b[0m-20.95   \u001b[0m | \u001b[0m 11.89   \u001b[0m | \u001b[0m-7.821   \u001b[0m | \u001b[0m-11.38   \u001b[0m | \u001b[0m-8.391   \u001b[0m |\n",
      "| \u001b[0m 175     \u001b[0m | \u001b[0m-15.46   \u001b[0m | \u001b[0m-5.937   \u001b[0m | \u001b[0m 11.49   \u001b[0m | \u001b[0m 1.295   \u001b[0m | \u001b[0m-6.399   \u001b[0m |\n",
      "| \u001b[0m 176     \u001b[0m | \u001b[0m-23.19   \u001b[0m | \u001b[0m-10.85   \u001b[0m | \u001b[0m-6.296   \u001b[0m | \u001b[0m 3.416   \u001b[0m | \u001b[0m-5.276   \u001b[0m |\n",
      "| \u001b[0m 177     \u001b[0m | \u001b[0m-9.643   \u001b[0m | \u001b[0m-1.279   \u001b[0m | \u001b[0m-1.676   \u001b[0m | \u001b[0m 9.844   \u001b[0m | \u001b[0m-2.21    \u001b[0m |\n",
      "| \u001b[0m 178     \u001b[0m | \u001b[0m-14.82   \u001b[0m | \u001b[0m-4.629   \u001b[0m | \u001b[0m 5.249   \u001b[0m | \u001b[0m-8.356   \u001b[0m | \u001b[0m-0.9826  \u001b[0m |\n",
      "| \u001b[0m 179     \u001b[0m | \u001b[0m-14.17   \u001b[0m | \u001b[0m-5.557   \u001b[0m | \u001b[0m 3.806   \u001b[0m | \u001b[0m 11.32   \u001b[0m | \u001b[0m-1.569   \u001b[0m |\n",
      "| \u001b[0m 180     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 8.534   \u001b[0m | \u001b[0m-1.291   \u001b[0m | \u001b[0m 4.687   \u001b[0m | \u001b[0m-11.04   \u001b[0m |\n",
      "| \u001b[0m 181     \u001b[0m | \u001b[0m-10.87   \u001b[0m | \u001b[0m-2.011   \u001b[0m | \u001b[0m 12.28   \u001b[0m | \u001b[0m 3.164   \u001b[0m | \u001b[0m 8.003   \u001b[0m |\n",
      "| \u001b[0m 182     \u001b[0m | \u001b[0m-20.23   \u001b[0m | \u001b[0m 0.141   \u001b[0m | \u001b[0m-0.05833 \u001b[0m | \u001b[0m 10.29   \u001b[0m | \u001b[0m-12.97   \u001b[0m |\n",
      "| \u001b[0m 183     \u001b[0m | \u001b[0m-17.97   \u001b[0m | \u001b[0m 2.198   \u001b[0m | \u001b[0m 13.28   \u001b[0m | \u001b[0m 0.5358  \u001b[0m | \u001b[0m-9.166   \u001b[0m |\n",
      "| \u001b[0m 184     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 10.44   \u001b[0m | \u001b[0m-7.451   \u001b[0m | \u001b[0m 6.022   \u001b[0m | \u001b[0m 1.208   \u001b[0m |\n",
      "| \u001b[0m 185     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 13.47   \u001b[0m | \u001b[0m 3.73    \u001b[0m | \u001b[0m 10.14   \u001b[0m | \u001b[0m-14.76   \u001b[0m |\n",
      "| \u001b[0m 186     \u001b[0m | \u001b[0m-20.36   \u001b[0m | \u001b[0m 14.68   \u001b[0m | \u001b[0m-12.67   \u001b[0m | \u001b[0m-5.336   \u001b[0m | \u001b[0m 13.38   \u001b[0m |\n",
      "| \u001b[0m 187     \u001b[0m | \u001b[0m-29.53   \u001b[0m | \u001b[0m-14.73   \u001b[0m | \u001b[0m 9.682   \u001b[0m | \u001b[0m 10.84   \u001b[0m | \u001b[0m-1.805   \u001b[0m |\n",
      "| \u001b[0m 188     \u001b[0m | \u001b[0m-20.49   \u001b[0m | \u001b[0m-7.328   \u001b[0m | \u001b[0m 9.081   \u001b[0m | \u001b[0m-0.6641  \u001b[0m | \u001b[0m-10.97   \u001b[0m |\n",
      "| \u001b[0m 189     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 12.84   \u001b[0m | \u001b[0m 11.88   \u001b[0m | \u001b[0m-0.2536  \u001b[0m | \u001b[0m 10.7    \u001b[0m |\n",
      "| \u001b[0m 190     \u001b[0m | \u001b[0m-13.13   \u001b[0m | \u001b[0m-2.443   \u001b[0m | \u001b[0m 5.504   \u001b[0m | \u001b[0m-3.06    \u001b[0m | \u001b[0m 0.1723  \u001b[0m |\n",
      "| \u001b[0m 191     \u001b[0m | \u001b[0m-22.61   \u001b[0m | \u001b[0m-9.313   \u001b[0m | \u001b[0m 13.95   \u001b[0m | \u001b[0m-6.174   \u001b[0m | \u001b[0m-11.9    \u001b[0m |\n",
      "| \u001b[0m 192     \u001b[0m | \u001b[0m-25.12   \u001b[0m | \u001b[0m-10.67   \u001b[0m | \u001b[0m-14.58   \u001b[0m | \u001b[0m 6.478   \u001b[0m | \u001b[0m 1.935   \u001b[0m |\n",
      "| \u001b[0m 193     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 8.837   \u001b[0m | \u001b[0m 0.2124  \u001b[0m | \u001b[0m 8.755   \u001b[0m | \u001b[0m 5.873   \u001b[0m |\n",
      "| \u001b[0m 194     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 8.335   \u001b[0m | \u001b[0m-2.806   \u001b[0m | \u001b[0m 4.433   \u001b[0m | \u001b[0m-9.606   \u001b[0m |\n",
      "| \u001b[0m 195     \u001b[0m | \u001b[0m-16.59   \u001b[0m | \u001b[0m-5.345   \u001b[0m | \u001b[0m-9.822   \u001b[0m | \u001b[0m-2.741   \u001b[0m | \u001b[0m-7.757   \u001b[0m |\n",
      "| \u001b[0m 196     \u001b[0m | \u001b[0m-10.92   \u001b[0m | \u001b[0m-2.792   \u001b[0m | \u001b[0m 14.26   \u001b[0m | \u001b[0m-5.39    \u001b[0m | \u001b[0m 14.47   \u001b[0m |\n",
      "| \u001b[0m 197     \u001b[0m | \u001b[0m-12.52   \u001b[0m | \u001b[0m 4.089   \u001b[0m | \u001b[0m-3.747   \u001b[0m | \u001b[0m 10.72   \u001b[0m | \u001b[0m 3.588   \u001b[0m |\n",
      "| \u001b[0m 198     \u001b[0m | \u001b[0m-17.15   \u001b[0m | \u001b[0m-7.439   \u001b[0m | \u001b[0m 8.786   \u001b[0m | \u001b[0m-2.012   \u001b[0m | \u001b[0m-4.275   \u001b[0m |\n",
      "| \u001b[0m 199     \u001b[0m | \u001b[0m-13.4    \u001b[0m | \u001b[0m-5.092   \u001b[0m | \u001b[0m 5.921   \u001b[0m | \u001b[0m-6.94    \u001b[0m | \u001b[0m 9.248   \u001b[0m |\n",
      "| \u001b[0m 200     \u001b[0m | \u001b[0m-14.08   \u001b[0m | \u001b[0m-6.141   \u001b[0m | \u001b[0m 1.324   \u001b[0m | \u001b[0m-0.3624  \u001b[0m | \u001b[0m 10.66   \u001b[0m |\n",
      "| \u001b[0m 201     \u001b[0m | \u001b[0m-27.78   \u001b[0m | \u001b[0m 11.65   \u001b[0m | \u001b[0m-9.468   \u001b[0m | \u001b[0m 2.56    \u001b[0m | \u001b[0m 11.95   \u001b[0m |\n",
      "| \u001b[0m 202     \u001b[0m | \u001b[0m-14.64   \u001b[0m | \u001b[0m-1.616   \u001b[0m | \u001b[0m 12.66   \u001b[0m | \u001b[0m-6.63    \u001b[0m | \u001b[0m 3.265   \u001b[0m |\n",
      "| \u001b[0m 203     \u001b[0m | \u001b[0m-21.8    \u001b[0m | \u001b[0m 5.474   \u001b[0m | \u001b[0m-8.154   \u001b[0m | \u001b[0m-14.59   \u001b[0m | \u001b[0m-2.498   \u001b[0m |\n",
      "| \u001b[0m 204     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 13.15   \u001b[0m | \u001b[0m-4.709   \u001b[0m | \u001b[0m 8.392   \u001b[0m | \u001b[0m-9.758   \u001b[0m |\n",
      "| \u001b[0m 205     \u001b[0m | \u001b[0m-16.22   \u001b[0m | \u001b[0m-4.741   \u001b[0m | \u001b[0m-10.66   \u001b[0m | \u001b[0m 6.503   \u001b[0m | \u001b[0m 5.979   \u001b[0m |\n",
      "| \u001b[0m 206     \u001b[0m | \u001b[0m-16.58   \u001b[0m | \u001b[0m 5.655   \u001b[0m | \u001b[0m-7.398   \u001b[0m | \u001b[0m 5.771   \u001b[0m | \u001b[0m-8.181   \u001b[0m |\n",
      "| \u001b[0m 207     \u001b[0m | \u001b[0m-17.91   \u001b[0m | \u001b[0m-2.261   \u001b[0m | \u001b[0m-3.842   \u001b[0m | \u001b[0m-4.341   \u001b[0m | \u001b[0m-13.27   \u001b[0m |\n",
      "| \u001b[0m 208     \u001b[0m | \u001b[0m-26.32   \u001b[0m | \u001b[0m 3.949   \u001b[0m | \u001b[0m 6.219   \u001b[0m | \u001b[0m 3.408   \u001b[0m | \u001b[0m 4.449   \u001b[0m |\n",
      "| \u001b[0m 209     \u001b[0m | \u001b[0m-21.22   \u001b[0m | \u001b[0m-9.902   \u001b[0m | \u001b[0m-10.52   \u001b[0m | \u001b[0m 0.4253  \u001b[0m | \u001b[0m 11.26   \u001b[0m |\n",
      "| \u001b[0m 210     \u001b[0m | \u001b[0m-20.33   \u001b[0m | \u001b[0m-9.481   \u001b[0m | \u001b[0m-1.115   \u001b[0m | \u001b[0m-2.132   \u001b[0m | \u001b[0m-0.08132 \u001b[0m |\n",
      "| \u001b[0m 211     \u001b[0m | \u001b[0m-22.45   \u001b[0m | \u001b[0m-10.15   \u001b[0m | \u001b[0m-4.727   \u001b[0m | \u001b[0m-7.144   \u001b[0m | \u001b[0m 10.34   \u001b[0m |\n",
      "| \u001b[0m 212     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 9.01    \u001b[0m | \u001b[0m-2.201   \u001b[0m | \u001b[0m 3.21    \u001b[0m | \u001b[0m-10.64   \u001b[0m |\n",
      "| \u001b[0m 213     \u001b[0m | \u001b[0m-13.8    \u001b[0m | \u001b[0m 0.2884  \u001b[0m | \u001b[0m-6.092   \u001b[0m | \u001b[0m 10.79   \u001b[0m | \u001b[0m 5.148   \u001b[0m |\n",
      "| \u001b[0m 214     \u001b[0m | \u001b[0m-20.64   \u001b[0m | \u001b[0m 4.004   \u001b[0m | \u001b[0m-11.26   \u001b[0m | \u001b[0m-0.8824  \u001b[0m | \u001b[0m 14.6    \u001b[0m |\n",
      "| \u001b[0m 215     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 13.45   \u001b[0m | \u001b[0m 4.353   \u001b[0m | \u001b[0m-10.45   \u001b[0m | \u001b[0m 4.174   \u001b[0m |\n",
      "| \u001b[0m 216     \u001b[0m | \u001b[0m-10.53   \u001b[0m | \u001b[0m 1.97    \u001b[0m | \u001b[0m-0.94    \u001b[0m | \u001b[0m-2.159   \u001b[0m | \u001b[0m 2.978   \u001b[0m |\n",
      "| \u001b[0m 217     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 10.5    \u001b[0m | \u001b[0m 7.534   \u001b[0m | \u001b[0m 2.381   \u001b[0m | \u001b[0m 12.74   \u001b[0m |\n",
      "| \u001b[0m 218     \u001b[0m | \u001b[0m-27.32   \u001b[0m | \u001b[0m-13.06   \u001b[0m | \u001b[0m 14.74   \u001b[0m | \u001b[0m-13.41   \u001b[0m | \u001b[0m-9.015   \u001b[0m |\n",
      "| \u001b[0m 219     \u001b[0m | \u001b[0m-18.52   \u001b[0m | \u001b[0m-2.317   \u001b[0m | \u001b[0m-11.77   \u001b[0m | \u001b[0m 3.71    \u001b[0m | \u001b[0m-13.56   \u001b[0m |\n",
      "| \u001b[0m 220     \u001b[0m | \u001b[0m-20.12   \u001b[0m | \u001b[0m-6.461   \u001b[0m | \u001b[0m-13.17   \u001b[0m | \u001b[0m 6.106   \u001b[0m | \u001b[0m 5.054   \u001b[0m |\n",
      "| \u001b[0m 221     \u001b[0m | \u001b[0m-14.59   \u001b[0m | \u001b[0m-3.643   \u001b[0m | \u001b[0m-9.354   \u001b[0m | \u001b[0m 7.41    \u001b[0m | \u001b[0m-4.789   \u001b[0m |\n",
      "| \u001b[0m 222     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 8.859   \u001b[0m | \u001b[0m-0.363   \u001b[0m | \u001b[0m 0.7701  \u001b[0m | \u001b[0m-14.15   \u001b[0m |\n",
      "| \u001b[0m 223     \u001b[0m | \u001b[0m-14.85   \u001b[0m | \u001b[0m 4.327   \u001b[0m | \u001b[0m-4.48    \u001b[0m | \u001b[0m-8.124   \u001b[0m | \u001b[0m-1.983   \u001b[0m |\n",
      "| \u001b[0m 224     \u001b[0m | \u001b[0m-11.31   \u001b[0m | \u001b[0m-3.526   \u001b[0m | \u001b[0m-0.9063  \u001b[0m | \u001b[0m 14.38   \u001b[0m | \u001b[0m-4.069   \u001b[0m |\n",
      "| \u001b[0m 225     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 8.232   \u001b[0m | \u001b[0m 1.583   \u001b[0m | \u001b[0m 11.67   \u001b[0m | \u001b[0m-4.351   \u001b[0m |\n",
      "| \u001b[0m 226     \u001b[0m | \u001b[0m-21.52   \u001b[0m | \u001b[0m-7.634   \u001b[0m | \u001b[0m 12.33   \u001b[0m | \u001b[0m-13.69   \u001b[0m | \u001b[0m 13.52   \u001b[0m |\n",
      "| \u001b[0m 227     \u001b[0m | \u001b[0m-20.27   \u001b[0m | \u001b[0m 1.692   \u001b[0m | \u001b[0m-3.709   \u001b[0m | \u001b[0m 14.85   \u001b[0m | \u001b[0m-13.25   \u001b[0m |\n",
      "| \u001b[0m 228     \u001b[0m | \u001b[0m-20.13   \u001b[0m | \u001b[0m 0.5012  \u001b[0m | \u001b[0m-14.07   \u001b[0m | \u001b[0m 2.135   \u001b[0m | \u001b[0m-9.586   \u001b[0m |\n",
      "| \u001b[0m 229     \u001b[0m | \u001b[0m-26.1    \u001b[0m | \u001b[0m 3.929   \u001b[0m | \u001b[0m 14.43   \u001b[0m | \u001b[0m 11.25   \u001b[0m | \u001b[0m-1.445   \u001b[0m |\n",
      "| \u001b[0m 230     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 6.254   \u001b[0m | \u001b[0m 8.324   \u001b[0m | \u001b[0m-0.1547  \u001b[0m | \u001b[0m 0.856   \u001b[0m |\n",
      "| \u001b[0m 231     \u001b[0m | \u001b[0m-22.28   \u001b[0m | \u001b[0m-10.48   \u001b[0m | \u001b[0m-3.918   \u001b[0m | \u001b[0m-10.73   \u001b[0m | \u001b[0m 6.807   \u001b[0m |\n",
      "| \u001b[0m 232     \u001b[0m | \u001b[0m-10.44   \u001b[0m | \u001b[0m-0.6896  \u001b[0m | \u001b[0m-1.534   \u001b[0m | \u001b[0m 11.58   \u001b[0m | \u001b[0m 0.8286  \u001b[0m |\n",
      "| \u001b[0m 233     \u001b[0m | \u001b[0m-16.68   \u001b[0m | \u001b[0m-2.727   \u001b[0m | \u001b[0m-6.933   \u001b[0m | \u001b[0m-12.84   \u001b[0m | \u001b[0m-2.456   \u001b[0m |\n",
      "| \u001b[0m 234     \u001b[0m | \u001b[0m-28.97   \u001b[0m | \u001b[0m-14.23   \u001b[0m | \u001b[0m-6.265   \u001b[0m | \u001b[0m 0.1053  \u001b[0m | \u001b[0m 13.98   \u001b[0m |\n",
      "| \u001b[0m 235     \u001b[0m | \u001b[0m-25.48   \u001b[0m | \u001b[0m-11.72   \u001b[0m | \u001b[0m 5.191   \u001b[0m | \u001b[0m-0.002029\u001b[0m | \u001b[0m 8.313   \u001b[0m |\n",
      "| \u001b[0m 236     \u001b[0m | \u001b[0m-23.5    \u001b[0m | \u001b[0m-10.69   \u001b[0m | \u001b[0m-12.5    \u001b[0m | \u001b[0m-3.023   \u001b[0m | \u001b[0m 8.909   \u001b[0m |\n",
      "| \u001b[0m 237     \u001b[0m | \u001b[0m-19.95   \u001b[0m | \u001b[0m-9.25    \u001b[0m | \u001b[0m 8.033   \u001b[0m | \u001b[0m-6.291   \u001b[0m | \u001b[0m-8.493   \u001b[0m |\n",
      "| \u001b[0m 238     \u001b[0m | \u001b[0m-29.48   \u001b[0m | \u001b[0m-14.5    \u001b[0m | \u001b[0m-3.04    \u001b[0m | \u001b[0m-3.568   \u001b[0m | \u001b[0m 4.78    \u001b[0m |\n",
      "| \u001b[0m 239     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m-12.87   \u001b[0m | \u001b[0m-10.42   \u001b[0m | \u001b[0m-14.5    \u001b[0m | \u001b[0m-11.59   \u001b[0m |\n",
      "| \u001b[0m 240     \u001b[0m | \u001b[0m-12.77   \u001b[0m | \u001b[0m 4.554   \u001b[0m | \u001b[0m-2.92    \u001b[0m | \u001b[0m-5.369   \u001b[0m | \u001b[0m 1.737   \u001b[0m |\n",
      "| \u001b[0m 241     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 14.8    \u001b[0m | \u001b[0m 10.03   \u001b[0m | \u001b[0m 5.989   \u001b[0m | \u001b[0m 12.55   \u001b[0m |\n",
      "| \u001b[0m 242     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m-13.81   \u001b[0m | \u001b[0m-12.89   \u001b[0m | \u001b[0m-0.7798  \u001b[0m | \u001b[0m-4.525   \u001b[0m |\n",
      "| \u001b[0m 243     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 13.12   \u001b[0m | \u001b[0m-0.3131  \u001b[0m | \u001b[0m 1.189   \u001b[0m | \u001b[0m 11.86   \u001b[0m |\n",
      "| \u001b[0m 244     \u001b[0m | \u001b[0m-15.69   \u001b[0m | \u001b[0m-1.601   \u001b[0m | \u001b[0m 11.31   \u001b[0m | \u001b[0m-7.393   \u001b[0m | \u001b[0m-6.786   \u001b[0m |\n",
      "| \u001b[0m 245     \u001b[0m | \u001b[0m-15.13   \u001b[0m | \u001b[0m-5.149   \u001b[0m | \u001b[0m 1.427   \u001b[0m | \u001b[0m-8.396   \u001b[0m | \u001b[0m 5.143   \u001b[0m |\n",
      "| \u001b[0m 246     \u001b[0m | \u001b[0m-23.53   \u001b[0m | \u001b[0m-10.72   \u001b[0m | \u001b[0m-12.18   \u001b[0m | \u001b[0m 11.11   \u001b[0m | \u001b[0m-7.894   \u001b[0m |\n",
      "| \u001b[0m 247     \u001b[0m | \u001b[0m-21.37   \u001b[0m | \u001b[0m-3.42    \u001b[0m | \u001b[0m 2.146   \u001b[0m | \u001b[0m 0.7741  \u001b[0m | \u001b[0m-12.72   \u001b[0m |\n",
      "| \u001b[0m 248     \u001b[0m | \u001b[0m-30.0    \u001b[0m | \u001b[0m 11.22   \u001b[0m | \u001b[0m 13.53   \u001b[0m | \u001b[0m 9.375   \u001b[0m | \u001b[0m-6.486   \u001b[0m |\n",
      "| \u001b[0m 249     \u001b[0m | \u001b[0m-11.9    \u001b[0m | \u001b[0m 0.8354  \u001b[0m | \u001b[0m-4.817   \u001b[0m | \u001b[0m 1.64    \u001b[0m | \u001b[0m 14.23   \u001b[0m |\n",
      "| \u001b[0m 250     \u001b[0m | \u001b[0m-13.92   \u001b[0m | \u001b[0m-5.649   \u001b[0m | \u001b[0m 5.064   \u001b[0m | \u001b[0m-5.221   \u001b[0m | \u001b[0m 8.234   \u001b[0m |\n",
      "| \u001b[0m 251     \u001b[0m | \u001b[0m-13.57   \u001b[0m | \u001b[0m-5.226   \u001b[0m | \u001b[0m 11.69   \u001b[0m | \u001b[0m 7.551   \u001b[0m | \u001b[0m 7.879   \u001b[0m |\n",
      "| \u001b[0m 252     \u001b[0m | \u001b[0m-16.74   \u001b[0m | \u001b[0m-0.9156  \u001b[0m | \u001b[0m-8.677   \u001b[0m | \u001b[0m-13.76   \u001b[0m | \u001b[0m-5.345   \u001b[0m |\n",
      "| \u001b[0m 253     \u001b[0m | \u001b[0m-28.3    \u001b[0m | \u001b[0m-13.89   \u001b[0m | \u001b[0m 5.816   \u001b[0m | \u001b[0m 5.111   \u001b[0m | \u001b[0m-2.086   \u001b[0m |\n",
      "| \u001b[0m 254     \u001b[0m | \u001b[0m-21.59   \u001b[0m | \u001b[0m 8.034   \u001b[0m | \u001b[0m 1.08    \u001b[0m | \u001b[0m-13.8    \u001b[0m | \u001b[0m-10.96   \u001b[0m |\n",
      "| \u001b[0m 255     \u001b[0m | \u001b[0m-22.84   \u001b[0m | \u001b[0m-9.198   \u001b[0m | \u001b[0m-4.93    \u001b[0m | \u001b[0m-13.43   \u001b[0m | \u001b[0m 3.154   \u001b[0m |\n",
      "| \u001b[0m 256     \u001b[0m | \u001b[0m-11.68   \u001b[0m | \u001b[0m 0.3618  \u001b[0m | \u001b[0m 3.524   \u001b[0m | \u001b[0m-2.029   \u001b[0m | \u001b[0m 10.43   \u001b[0m |\n",
      "| \u001b[0m 257     \u001b[0m | \u001b[0m-9.957   \u001b[0m | \u001b[0m-0.6418  \u001b[0m | \u001b[0m 7.169   \u001b[0m | \u001b[0m 0.3508  \u001b[0m | \u001b[0m 8.156   \u001b[0m |\n",
      "| \u001b[95m 258     \u001b[0m | \u001b[95m-7.15    \u001b[0m | \u001b[95m-1.271   \u001b[0m | \u001b[95m-2.813   \u001b[0m | \u001b[95m-1.098   \u001b[0m | \u001b[95m 10.62   \u001b[0m |\n",
      "| \u001b[0m 259     \u001b[0m | \u001b[0m-7.43    \u001b[0m | \u001b[0m-1.395   \u001b[0m | \u001b[0m-2.664   \u001b[0m | \u001b[0m-2.088   \u001b[0m | \u001b[0m 11.36   \u001b[0m |\n",
      "| \u001b[0m 260     \u001b[0m | \u001b[0m-9.549   \u001b[0m | \u001b[0m-1.103   \u001b[0m | \u001b[0m-2.367   \u001b[0m | \u001b[0m-1.58    \u001b[0m | \u001b[0m 9.615   \u001b[0m |\n",
      "| \u001b[0m 261     \u001b[0m | \u001b[0m-7.695   \u001b[0m | \u001b[0m-1.892   \u001b[0m | \u001b[0m-3.424   \u001b[0m | \u001b[0m-0.4887  \u001b[0m | \u001b[0m 12.14   \u001b[0m |\n",
      "| \u001b[0m 262     \u001b[0m | \u001b[0m-10.27   \u001b[0m | \u001b[0m-0.9738  \u001b[0m | \u001b[0m-2.541   \u001b[0m | \u001b[0m-0.9354  \u001b[0m | \u001b[0m 11.85   \u001b[0m |\n",
      "| \u001b[0m 263     \u001b[0m | \u001b[0m-9.087   \u001b[0m | \u001b[0m-2.496   \u001b[0m | \u001b[0m-4.469   \u001b[0m | \u001b[0m-1.57    \u001b[0m | \u001b[0m 10.81   \u001b[0m |\n",
      "| \u001b[0m 264     \u001b[0m | \u001b[0m-8.746   \u001b[0m | \u001b[0m-2.515   \u001b[0m | \u001b[0m-2.738   \u001b[0m | \u001b[0m-1.678   \u001b[0m | \u001b[0m 11.29   \u001b[0m |\n",
      "| \u001b[0m 265     \u001b[0m | \u001b[0m-10.37   \u001b[0m | \u001b[0m-1.126   \u001b[0m | \u001b[0m-4.068   \u001b[0m | \u001b[0m-1.659   \u001b[0m | \u001b[0m 11.32   \u001b[0m |\n",
      "| \u001b[0m 266     \u001b[0m | \u001b[0m-8.25    \u001b[0m | \u001b[0m-2.309   \u001b[0m | \u001b[0m-3.708   \u001b[0m | \u001b[0m 1.181   \u001b[0m | \u001b[0m 10.83   \u001b[0m |\n",
      "| \u001b[0m 267     \u001b[0m | \u001b[0m-8.551   \u001b[0m | \u001b[0m-2.231   \u001b[0m | \u001b[0m-3.124   \u001b[0m | \u001b[0m-0.1824  \u001b[0m | \u001b[0m 10.87   \u001b[0m |\n",
      "| \u001b[0m 268     \u001b[0m | \u001b[0m-9.319   \u001b[0m | \u001b[0m-2.634   \u001b[0m | \u001b[0m-4.476   \u001b[0m | \u001b[0m 0.6627  \u001b[0m | \u001b[0m 12.47   \u001b[0m |\n",
      "| \u001b[0m 269     \u001b[0m | \u001b[0m-7.832   \u001b[0m | \u001b[0m-1.806   \u001b[0m | \u001b[0m-1.839   \u001b[0m | \u001b[0m-3.309   \u001b[0m | \u001b[0m 11.4    \u001b[0m |\n",
      "| \u001b[0m 270     \u001b[0m | \u001b[0m-7.893   \u001b[0m | \u001b[0m-1.811   \u001b[0m | \u001b[0m-1.911   \u001b[0m | \u001b[0m-2.851   \u001b[0m | \u001b[0m 11.18   \u001b[0m |\n",
      "| \u001b[0m 271     \u001b[0m | \u001b[0m-8.119   \u001b[0m | \u001b[0m-1.908   \u001b[0m | \u001b[0m-2.172   \u001b[0m | \u001b[0m-3.498   \u001b[0m | \u001b[0m 11.77   \u001b[0m |\n",
      "| \u001b[0m 272     \u001b[0m | \u001b[0m-8.647   \u001b[0m | \u001b[0m-1.803   \u001b[0m | \u001b[0m-2.103   \u001b[0m | \u001b[0m-2.663   \u001b[0m | \u001b[0m 11.11   \u001b[0m |\n",
      "| \u001b[0m 273     \u001b[0m | \u001b[0m-12.91   \u001b[0m | \u001b[0m-1.629   \u001b[0m | \u001b[0m-0.7833  \u001b[0m | \u001b[0m-4.211   \u001b[0m | \u001b[0m 11.82   \u001b[0m |\n",
      "| \u001b[0m 274     \u001b[0m | \u001b[0m-8.51    \u001b[0m | \u001b[0m-2.156   \u001b[0m | \u001b[0m-3.017   \u001b[0m | \u001b[0m-3.226   \u001b[0m | \u001b[0m 11.46   \u001b[0m |\n",
      "| \u001b[0m 275     \u001b[0m | \u001b[0m-8.989   \u001b[0m | \u001b[0m-1.899   \u001b[0m | \u001b[0m-2.533   \u001b[0m | \u001b[0m-2.552   \u001b[0m | \u001b[0m 11.55   \u001b[0m |\n",
      "| \u001b[0m 276     \u001b[0m | \u001b[0m-7.772   \u001b[0m | \u001b[0m-1.419   \u001b[0m | \u001b[0m-3.589   \u001b[0m | \u001b[0m 0.7831  \u001b[0m | \u001b[0m 10.58   \u001b[0m |\n",
      "| \u001b[0m 277     \u001b[0m | \u001b[0m-9.322   \u001b[0m | \u001b[0m-1.114   \u001b[0m | \u001b[0m-3.69    \u001b[0m | \u001b[0m 1.434   \u001b[0m | \u001b[0m 10.1    \u001b[0m |\n",
      "| \u001b[0m 278     \u001b[0m | \u001b[0m-8.339   \u001b[0m | \u001b[0m-1.801   \u001b[0m | \u001b[0m-3.604   \u001b[0m | \u001b[0m 0.2439  \u001b[0m | \u001b[0m 11.16   \u001b[0m |\n",
      "| \u001b[0m 279     \u001b[0m | \u001b[0m-8.035   \u001b[0m | \u001b[0m-1.737   \u001b[0m | \u001b[0m-2.497   \u001b[0m | \u001b[0m-2.94    \u001b[0m | \u001b[0m 10.89   \u001b[0m |\n",
      "| \u001b[0m 280     \u001b[0m | \u001b[0m-7.583   \u001b[0m | \u001b[0m-1.789   \u001b[0m | \u001b[0m-3.163   \u001b[0m | \u001b[0m-0.2109  \u001b[0m | \u001b[0m 10.71   \u001b[0m |\n",
      "| \u001b[95m 281     \u001b[0m | \u001b[95m-7.131   \u001b[0m | \u001b[95m-1.766   \u001b[0m | \u001b[95m-3.086   \u001b[0m | \u001b[95m-0.2441  \u001b[0m | \u001b[95m 10.58   \u001b[0m |\n",
      "| \u001b[0m 282     \u001b[0m | \u001b[0m-8.207   \u001b[0m | \u001b[0m-1.767   \u001b[0m | \u001b[0m-2.963   \u001b[0m | \u001b[0m-0.1634  \u001b[0m | \u001b[0m 10.36   \u001b[0m |\n",
      "| \u001b[0m 283     \u001b[0m | \u001b[0m-7.544   \u001b[0m | \u001b[0m-1.764   \u001b[0m | \u001b[0m-3.215   \u001b[0m | \u001b[0m-0.351   \u001b[0m | \u001b[0m 10.64   \u001b[0m |\n",
      "| \u001b[0m 284     \u001b[0m | \u001b[0m-7.951   \u001b[0m | \u001b[0m-1.765   \u001b[0m | \u001b[0m-3.257   \u001b[0m | \u001b[0m-0.3612  \u001b[0m | \u001b[0m 10.61   \u001b[0m |\n",
      "| \u001b[0m 285     \u001b[0m | \u001b[0m-7.402   \u001b[0m | \u001b[0m-1.76    \u001b[0m | \u001b[0m-3.13    \u001b[0m | \u001b[0m-0.3512  \u001b[0m | \u001b[0m 10.68   \u001b[0m |\n",
      "| \u001b[0m 286     \u001b[0m | \u001b[0m-7.377   \u001b[0m | \u001b[0m-1.757   \u001b[0m | \u001b[0m-3.105   \u001b[0m | \u001b[0m-0.4009  \u001b[0m | \u001b[0m 10.68   \u001b[0m |\n",
      "| \u001b[0m 287     \u001b[0m | \u001b[0m-7.415   \u001b[0m | \u001b[0m-1.758   \u001b[0m | \u001b[0m-3.074   \u001b[0m | \u001b[0m-0.4558  \u001b[0m | \u001b[0m 10.68   \u001b[0m |\n",
      "| \u001b[0m 288     \u001b[0m | \u001b[0m-8.247   \u001b[0m | \u001b[0m-1.762   \u001b[0m | \u001b[0m-3.041   \u001b[0m | \u001b[0m-0.5027  \u001b[0m | \u001b[0m 10.68   \u001b[0m |\n",
      "| \u001b[0m 289     \u001b[0m | \u001b[0m-7.999   \u001b[0m | \u001b[0m-1.751   \u001b[0m | \u001b[0m-3.258   \u001b[0m | \u001b[0m-0.2685  \u001b[0m | \u001b[0m 10.65   \u001b[0m |\n",
      "| \u001b[0m 290     \u001b[0m | \u001b[0m-8.394   \u001b[0m | \u001b[0m-1.747   \u001b[0m | \u001b[0m-2.999   \u001b[0m | \u001b[0m-0.1815  \u001b[0m | \u001b[0m 10.74   \u001b[0m |\n",
      "| \u001b[0m 291     \u001b[0m | \u001b[0m-7.677   \u001b[0m | \u001b[0m-1.818   \u001b[0m | \u001b[0m-3.406   \u001b[0m | \u001b[0m-0.3207  \u001b[0m | \u001b[0m 10.54   \u001b[0m |\n",
      "| \u001b[0m 292     \u001b[0m | \u001b[0m-8.534   \u001b[0m | \u001b[0m-1.823   \u001b[0m | \u001b[0m-3.433   \u001b[0m | \u001b[0m-0.298   \u001b[0m | \u001b[0m 10.52   \u001b[0m |\n",
      "| \u001b[0m 293     \u001b[0m | \u001b[0m-8.18    \u001b[0m | \u001b[0m-1.794   \u001b[0m | \u001b[0m-2.938   \u001b[0m | \u001b[0m-0.6708  \u001b[0m | \u001b[0m 10.73   \u001b[0m |\n",
      "| \u001b[0m 294     \u001b[0m | \u001b[0m-7.435   \u001b[0m | \u001b[0m-1.758   \u001b[0m | \u001b[0m-3.157   \u001b[0m | \u001b[0m-0.06068 \u001b[0m | \u001b[0m 10.84   \u001b[0m |\n",
      "| \u001b[0m 295     \u001b[0m | \u001b[0m-7.918   \u001b[0m | \u001b[0m-1.758   \u001b[0m | \u001b[0m-3.111   \u001b[0m | \u001b[0m 0.0895  \u001b[0m | \u001b[0m 10.91   \u001b[0m |\n",
      "| \u001b[0m 296     \u001b[0m | \u001b[0m-7.915   \u001b[0m | \u001b[0m-1.758   \u001b[0m | \u001b[0m-3.156   \u001b[0m | \u001b[0m-0.007031\u001b[0m | \u001b[0m 10.88   \u001b[0m |\n",
      "| \u001b[0m 297     \u001b[0m | \u001b[0m-7.692   \u001b[0m | \u001b[0m-1.761   \u001b[0m | \u001b[0m-3.181   \u001b[0m | \u001b[0m-0.07628 \u001b[0m | \u001b[0m 10.86   \u001b[0m |\n",
      "| \u001b[0m 298     \u001b[0m | \u001b[0m-8.272   \u001b[0m | \u001b[0m-1.762   \u001b[0m | \u001b[0m-3.17    \u001b[0m | \u001b[0m-0.04308 \u001b[0m | \u001b[0m 10.87   \u001b[0m |\n",
      "| \u001b[0m 299     \u001b[0m | \u001b[0m-7.534   \u001b[0m | \u001b[0m-1.776   \u001b[0m | \u001b[0m-3.119   \u001b[0m | \u001b[0m-0.4176  \u001b[0m | \u001b[0m 10.75   \u001b[0m |\n",
      "| \u001b[0m 300     \u001b[0m | \u001b[0m-7.669   \u001b[0m | \u001b[0m-1.787   \u001b[0m | \u001b[0m-3.079   \u001b[0m | \u001b[0m-0.5072  \u001b[0m | \u001b[0m 10.74   \u001b[0m |\n",
      "| \u001b[0m 301     \u001b[0m | \u001b[0m-7.538   \u001b[0m | \u001b[0m-1.794   \u001b[0m | \u001b[0m-3.056   \u001b[0m | \u001b[0m-0.5515  \u001b[0m | \u001b[0m 10.74   \u001b[0m |\n",
      "| \u001b[0m 302     \u001b[0m | \u001b[0m-8.015   \u001b[0m | \u001b[0m-1.806   \u001b[0m | \u001b[0m-3.016   \u001b[0m | \u001b[0m-0.6301  \u001b[0m | \u001b[0m 10.73   \u001b[0m |\n",
      "| \u001b[0m 303     \u001b[0m | \u001b[0m-8.295   \u001b[0m | \u001b[0m-1.793   \u001b[0m | \u001b[0m-3.093   \u001b[0m | \u001b[0m-0.4981  \u001b[0m | \u001b[0m 10.76   \u001b[0m |\n",
      "| \u001b[0m 304     \u001b[0m | \u001b[0m-7.939   \u001b[0m | \u001b[0m-1.758   \u001b[0m | \u001b[0m-3.237   \u001b[0m | \u001b[0m-0.198   \u001b[0m | \u001b[0m 10.76   \u001b[0m |\n",
      "| \u001b[0m 305     \u001b[0m | \u001b[0m-7.663   \u001b[0m | \u001b[0m-1.747   \u001b[0m | \u001b[0m-3.035   \u001b[0m | \u001b[0m-0.2139  \u001b[0m | \u001b[0m 10.66   \u001b[0m |\n",
      "| \u001b[0m 306     \u001b[0m | \u001b[0m-8.192   \u001b[0m | \u001b[0m-1.748   \u001b[0m | \u001b[0m-2.993   \u001b[0m | \u001b[0m-0.1791  \u001b[0m | \u001b[0m 10.64   \u001b[0m |\n",
      "| \u001b[0m 307     \u001b[0m | \u001b[0m-7.392   \u001b[0m | \u001b[0m-1.815   \u001b[0m | \u001b[0m-3.301   \u001b[0m | \u001b[0m-0.2465  \u001b[0m | \u001b[0m 10.82   \u001b[0m |\n",
      "| \u001b[0m 308     \u001b[0m | \u001b[0m-8.134   \u001b[0m | \u001b[0m-1.839   \u001b[0m | \u001b[0m-3.38    \u001b[0m | \u001b[0m-0.2     \u001b[0m | \u001b[0m 10.86   \u001b[0m |\n",
      "| \u001b[0m 309     \u001b[0m | \u001b[0m-8.217   \u001b[0m | \u001b[0m-1.807   \u001b[0m | \u001b[0m-3.263   \u001b[0m | \u001b[0m-0.2972  \u001b[0m | \u001b[0m 10.8    \u001b[0m |\n",
      "| \u001b[0m 310     \u001b[0m | \u001b[0m-8.264   \u001b[0m | \u001b[0m-1.792   \u001b[0m | \u001b[0m-3.026   \u001b[0m | \u001b[0m-0.4912  \u001b[0m | \u001b[0m 10.68   \u001b[0m |\n",
      "| \u001b[0m 311     \u001b[0m | \u001b[0m-7.722   \u001b[0m | \u001b[0m-1.77    \u001b[0m | \u001b[0m-3.291   \u001b[0m | \u001b[0m-0.08641 \u001b[0m | \u001b[0m 10.78   \u001b[0m |\n",
      "| \u001b[0m 312     \u001b[0m | \u001b[0m-7.573   \u001b[0m | \u001b[0m-1.776   \u001b[0m | \u001b[0m-3.312   \u001b[0m | \u001b[0m-0.0663  \u001b[0m | \u001b[0m 10.79   \u001b[0m |\n",
      "| \u001b[0m 313     \u001b[0m | \u001b[0m-7.523   \u001b[0m | \u001b[0m-1.782   \u001b[0m | \u001b[0m-3.355   \u001b[0m | \u001b[0m-0.03264 \u001b[0m | \u001b[0m 10.81   \u001b[0m |\n",
      "| \u001b[0m 314     \u001b[0m | \u001b[0m-7.429   \u001b[0m | \u001b[0m-1.787   \u001b[0m | \u001b[0m-3.407   \u001b[0m | \u001b[0m 0.000237\u001b[0m | \u001b[0m 10.84   \u001b[0m |\n",
      "| \u001b[0m 315     \u001b[0m | \u001b[0m-7.826   \u001b[0m | \u001b[0m-1.792   \u001b[0m | \u001b[0m-3.475   \u001b[0m | \u001b[0m 0.03584 \u001b[0m | \u001b[0m 10.86   \u001b[0m |\n",
      "| \u001b[0m 316     \u001b[0m | \u001b[0m-8.744   \u001b[0m | \u001b[0m-1.793   \u001b[0m | \u001b[0m-3.462   \u001b[0m | \u001b[0m 0.03174 \u001b[0m | \u001b[0m 10.86   \u001b[0m |\n",
      "| \u001b[0m 317     \u001b[0m | \u001b[0m-8.04    \u001b[0m | \u001b[0m-1.775   \u001b[0m | \u001b[0m-3.091   \u001b[0m | \u001b[0m-0.01334 \u001b[0m | \u001b[0m 10.72   \u001b[0m |\n",
      "| \u001b[0m 318     \u001b[0m | \u001b[0m-7.665   \u001b[0m | \u001b[0m-1.719   \u001b[0m | \u001b[0m-3.192   \u001b[0m | \u001b[0m-0.1581  \u001b[0m | \u001b[0m 10.65   \u001b[0m |\n",
      "| \u001b[0m 319     \u001b[0m | \u001b[0m-7.908   \u001b[0m | \u001b[0m-1.709   \u001b[0m | \u001b[0m-3.189   \u001b[0m | \u001b[0m-0.1408  \u001b[0m | \u001b[0m 10.61   \u001b[0m |\n",
      "| \u001b[0m 320     \u001b[0m | \u001b[0m-7.576   \u001b[0m | \u001b[0m-1.734   \u001b[0m | \u001b[0m-3.177   \u001b[0m | \u001b[0m-0.1343  \u001b[0m | \u001b[0m 10.64   \u001b[0m |\n",
      "| \u001b[0m 321     \u001b[0m | \u001b[0m-7.914   \u001b[0m | \u001b[0m-1.735   \u001b[0m | \u001b[0m-3.165   \u001b[0m | \u001b[0m-0.09943 \u001b[0m | \u001b[0m 10.59   \u001b[0m |\n",
      "| \u001b[0m 322     \u001b[0m | \u001b[0m-7.709   \u001b[0m | \u001b[0m-1.748   \u001b[0m | \u001b[0m-3.141   \u001b[0m | \u001b[0m-0.08986 \u001b[0m | \u001b[0m 10.65   \u001b[0m |\n",
      "| \u001b[0m 323     \u001b[0m | \u001b[0m-7.511   \u001b[0m | \u001b[0m-1.765   \u001b[0m | \u001b[0m-3.116   \u001b[0m | \u001b[0m-0.05854 \u001b[0m | \u001b[0m 10.65   \u001b[0m |\n",
      "| \u001b[0m 324     \u001b[0m | \u001b[0m-8.119   \u001b[0m | \u001b[0m-1.779   \u001b[0m | \u001b[0m-3.069   \u001b[0m | \u001b[0m 0.003157\u001b[0m | \u001b[0m 10.63   \u001b[0m |\n",
      "| \u001b[0m 325     \u001b[0m | \u001b[0m-7.854   \u001b[0m | \u001b[0m-1.729   \u001b[0m | \u001b[0m-3.228   \u001b[0m | \u001b[0m-0.1549  \u001b[0m | \u001b[0m 10.59   \u001b[0m |\n",
      "| \u001b[0m 326     \u001b[0m | \u001b[0m-8.084   \u001b[0m | \u001b[0m-1.75    \u001b[0m | \u001b[0m-3.224   \u001b[0m | \u001b[0m-0.1486  \u001b[0m | \u001b[0m 10.59   \u001b[0m |\n",
      "| \u001b[0m 327     \u001b[0m | \u001b[0m-8.104   \u001b[0m | \u001b[0m-1.826   \u001b[0m | \u001b[0m-3.135   \u001b[0m | \u001b[0m-0.07994 \u001b[0m | \u001b[0m 10.84   \u001b[0m |\n",
      "| \u001b[0m 328     \u001b[0m | \u001b[0m-7.333   \u001b[0m | \u001b[0m-1.699   \u001b[0m | \u001b[0m-3.231   \u001b[0m | \u001b[0m-0.24    \u001b[0m | \u001b[0m 10.64   \u001b[0m |\n",
      "| \u001b[0m 329     \u001b[0m | \u001b[0m-8.726   \u001b[0m | \u001b[0m-1.657   \u001b[0m | \u001b[0m-3.259   \u001b[0m | \u001b[0m-0.2317  \u001b[0m | \u001b[0m 10.59   \u001b[0m |\n",
      "| \u001b[0m 330     \u001b[0m | \u001b[0m-8.074   \u001b[0m | \u001b[0m-1.919   \u001b[0m | \u001b[0m-3.126   \u001b[0m | \u001b[0m-0.1364  \u001b[0m | \u001b[0m 10.87   \u001b[0m |\n",
      "| \u001b[0m 331     \u001b[0m | \u001b[0m-7.761   \u001b[0m | \u001b[0m-1.888   \u001b[0m | \u001b[0m-3.141   \u001b[0m | \u001b[0m-0.2461  \u001b[0m | \u001b[0m 10.82   \u001b[0m |\n",
      "| \u001b[0m 332     \u001b[0m | \u001b[0m-7.658   \u001b[0m | \u001b[0m-1.902   \u001b[0m | \u001b[0m-3.137   \u001b[0m | \u001b[0m-0.2312  \u001b[0m | \u001b[0m 10.83   \u001b[0m |\n",
      "| \u001b[0m 333     \u001b[0m | \u001b[0m-7.842   \u001b[0m | \u001b[0m-1.928   \u001b[0m | \u001b[0m-3.129   \u001b[0m | \u001b[0m-0.2095  \u001b[0m | \u001b[0m 10.85   \u001b[0m |\n",
      "| \u001b[0m 334     \u001b[0m | \u001b[0m-8.283   \u001b[0m | \u001b[0m-1.928   \u001b[0m | \u001b[0m-3.126   \u001b[0m | \u001b[0m-0.2112  \u001b[0m | \u001b[0m 10.84   \u001b[0m |\n",
      "| \u001b[0m 335     \u001b[0m | \u001b[0m-8.88    \u001b[0m | \u001b[0m-1.812   \u001b[0m | \u001b[0m-3.121   \u001b[0m | \u001b[0m-0.3948  \u001b[0m | \u001b[0m 10.74   \u001b[0m |\n",
      "| \u001b[0m 336     \u001b[0m | \u001b[0m-7.753   \u001b[0m | \u001b[0m-1.737   \u001b[0m | \u001b[0m-3.164   \u001b[0m | \u001b[0m 0.08951 \u001b[0m | \u001b[0m 10.79   \u001b[0m |\n",
      "| \u001b[0m 337     \u001b[0m | \u001b[0m-7.454   \u001b[0m | \u001b[0m-1.74    \u001b[0m | \u001b[0m-3.148   \u001b[0m | \u001b[0m 0.1193  \u001b[0m | \u001b[0m 10.8    \u001b[0m |\n",
      "| \u001b[0m 338     \u001b[0m | \u001b[0m-7.742   \u001b[0m | \u001b[0m-1.742   \u001b[0m | \u001b[0m-3.098   \u001b[0m | \u001b[0m 0.2291  \u001b[0m | \u001b[0m 10.85   \u001b[0m |\n",
      "| \u001b[0m 339     \u001b[0m | \u001b[0m-8.167   \u001b[0m | \u001b[0m-1.746   \u001b[0m | \u001b[0m-3.081   \u001b[0m | \u001b[0m 0.2648  \u001b[0m | \u001b[0m 10.86   \u001b[0m |\n",
      "| \u001b[0m 340     \u001b[0m | \u001b[0m-7.632   \u001b[0m | \u001b[0m-1.723   \u001b[0m | \u001b[0m-3.151   \u001b[0m | \u001b[0m 0.1221  \u001b[0m | \u001b[0m 10.79   \u001b[0m |\n",
      "| \u001b[0m 341     \u001b[0m | \u001b[0m-9.407   \u001b[0m | \u001b[0m-1.722   \u001b[0m | \u001b[0m-3.126   \u001b[0m | \u001b[0m 0.1646  \u001b[0m | \u001b[0m 10.81   \u001b[0m |\n",
      "| \u001b[0m 342     \u001b[0m | \u001b[0m-8.042   \u001b[0m | \u001b[0m-1.874   \u001b[0m | \u001b[0m-3.364   \u001b[0m | \u001b[0m-0.1529  \u001b[0m | \u001b[0m 10.6    \u001b[0m |\n",
      "| \u001b[0m 343     \u001b[0m | \u001b[0m-8.224   \u001b[0m | \u001b[0m-1.851   \u001b[0m | \u001b[0m-3.339   \u001b[0m | \u001b[0m-0.1912  \u001b[0m | \u001b[0m 10.65   \u001b[0m |\n",
      "| \u001b[0m 344     \u001b[0m | \u001b[0m-8.184   \u001b[0m | \u001b[0m-1.774   \u001b[0m | \u001b[0m-3.218   \u001b[0m | \u001b[0m-0.3495  \u001b[0m | \u001b[0m 10.75   \u001b[0m |\n",
      "| \u001b[0m 345     \u001b[0m | \u001b[0m-8.077   \u001b[0m | \u001b[0m-1.844   \u001b[0m | \u001b[0m-3.219   \u001b[0m | \u001b[0m-0.2218  \u001b[0m | \u001b[0m 10.62   \u001b[0m |\n",
      "| \u001b[0m 346     \u001b[0m | \u001b[0m-7.758   \u001b[0m | \u001b[0m-1.752   \u001b[0m | \u001b[0m-3.22    \u001b[0m | \u001b[0m-0.2825  \u001b[0m | \u001b[0m 10.79   \u001b[0m |\n",
      "| \u001b[0m 347     \u001b[0m | \u001b[0m-7.913   \u001b[0m | \u001b[0m-1.754   \u001b[0m | \u001b[0m-3.241   \u001b[0m | \u001b[0m-0.2686  \u001b[0m | \u001b[0m 10.8    \u001b[0m |\n",
      "| \u001b[0m 348     \u001b[0m | \u001b[0m-7.543   \u001b[0m | \u001b[0m-1.764   \u001b[0m | \u001b[0m-3.236   \u001b[0m | \u001b[0m-0.2765  \u001b[0m | \u001b[0m 10.8    \u001b[0m |\n",
      "| \u001b[0m 349     \u001b[0m | \u001b[0m-7.473   \u001b[0m | \u001b[0m-1.762   \u001b[0m | \u001b[0m-3.265   \u001b[0m | \u001b[0m-0.2671  \u001b[0m | \u001b[0m 10.83   \u001b[0m |\n",
      "| \u001b[95m 350     \u001b[0m | \u001b[95m-7.075   \u001b[0m | \u001b[95m-1.76    \u001b[0m | \u001b[95m-3.299   \u001b[0m | \u001b[95m-0.2517  \u001b[0m | \u001b[95m 10.87   \u001b[0m |\n",
      "| \u001b[0m 351     \u001b[0m | \u001b[0m-7.716   \u001b[0m | \u001b[0m-1.751   \u001b[0m | \u001b[0m-3.366   \u001b[0m | \u001b[0m-0.2236  \u001b[0m | \u001b[0m 10.93   \u001b[0m |\n",
      "| \u001b[0m 352     \u001b[0m | \u001b[0m-7.891   \u001b[0m | \u001b[0m-1.752   \u001b[0m | \u001b[0m-3.381   \u001b[0m | \u001b[0m-0.2132  \u001b[0m | \u001b[0m 10.94   \u001b[0m |\n",
      "| \u001b[0m 353     \u001b[0m | \u001b[0m-8.355   \u001b[0m | \u001b[0m-1.753   \u001b[0m | \u001b[0m-3.373   \u001b[0m | \u001b[0m-0.2189  \u001b[0m | \u001b[0m 10.93   \u001b[0m |\n",
      "| \u001b[0m 354     \u001b[0m | \u001b[0m-7.428   \u001b[0m | \u001b[0m-1.765   \u001b[0m | \u001b[0m-3.293   \u001b[0m | \u001b[0m-0.2833  \u001b[0m | \u001b[0m 10.88   \u001b[0m |\n",
      "| \u001b[0m 355     \u001b[0m | \u001b[0m-7.421   \u001b[0m | \u001b[0m-1.767   \u001b[0m | \u001b[0m-3.321   \u001b[0m | \u001b[0m-0.2765  \u001b[0m | \u001b[0m 10.91   \u001b[0m |\n",
      "| \u001b[0m 356     \u001b[0m | \u001b[0m-7.663   \u001b[0m | \u001b[0m-1.768   \u001b[0m | \u001b[0m-3.352   \u001b[0m | \u001b[0m-0.2656  \u001b[0m | \u001b[0m 10.94   \u001b[0m |\n",
      "| \u001b[0m 357     \u001b[0m | \u001b[0m-7.308   \u001b[0m | \u001b[0m-1.77    \u001b[0m | \u001b[0m-3.367   \u001b[0m | \u001b[0m-0.2581  \u001b[0m | \u001b[0m 10.96   \u001b[0m |\n",
      "| \u001b[0m 358     \u001b[0m | \u001b[0m-7.272   \u001b[0m | \u001b[0m-1.771   \u001b[0m | \u001b[0m-3.412   \u001b[0m | \u001b[0m-0.2354  \u001b[0m | \u001b[0m 11.0    \u001b[0m |\n",
      "| \u001b[0m 359     \u001b[0m | \u001b[0m-7.817   \u001b[0m | \u001b[0m-1.772   \u001b[0m | \u001b[0m-3.465   \u001b[0m | \u001b[0m-0.2042  \u001b[0m | \u001b[0m 11.05   \u001b[0m |\n",
      "| \u001b[0m 360     \u001b[0m | \u001b[0m-8.461   \u001b[0m | \u001b[0m-1.773   \u001b[0m | \u001b[0m-3.461   \u001b[0m | \u001b[0m-0.2066  \u001b[0m | \u001b[0m 11.04   \u001b[0m |\n",
      "| \u001b[0m 361     \u001b[0m | \u001b[0m-7.899   \u001b[0m | \u001b[0m-1.776   \u001b[0m | \u001b[0m-3.366   \u001b[0m | \u001b[0m-0.2833  \u001b[0m | \u001b[0m 10.98   \u001b[0m |\n",
      "| \u001b[0m 362     \u001b[0m | \u001b[0m-7.466   \u001b[0m | \u001b[0m-1.778   \u001b[0m | \u001b[0m-3.36    \u001b[0m | \u001b[0m-0.2864  \u001b[0m | \u001b[0m 10.97   \u001b[0m |\n",
      "| \u001b[0m 363     \u001b[0m | \u001b[0m-8.353   \u001b[0m | \u001b[0m-1.784   \u001b[0m | \u001b[0m-3.379   \u001b[0m | \u001b[0m-0.28    \u001b[0m | \u001b[0m 10.99   \u001b[0m |\n",
      "| \u001b[0m 364     \u001b[0m | \u001b[0m-7.721   \u001b[0m | \u001b[0m-1.77    \u001b[0m | \u001b[0m-3.339   \u001b[0m | \u001b[0m-0.2981  \u001b[0m | \u001b[0m 10.95   \u001b[0m |\n",
      "| \u001b[0m 365     \u001b[0m | \u001b[0m-7.492   \u001b[0m | \u001b[0m-1.774   \u001b[0m | \u001b[0m-3.342   \u001b[0m | \u001b[0m-0.2998  \u001b[0m | \u001b[0m 10.95   \u001b[0m |\n",
      "| \u001b[0m 366     \u001b[0m | \u001b[0m-7.894   \u001b[0m | \u001b[0m-1.781   \u001b[0m | \u001b[0m-3.356   \u001b[0m | \u001b[0m-0.2987  \u001b[0m | \u001b[0m 10.97   \u001b[0m |\n",
      "| \u001b[0m 367     \u001b[0m | \u001b[0m-10.21   \u001b[0m | \u001b[0m-1.78    \u001b[0m | \u001b[0m-3.351   \u001b[0m | \u001b[0m-0.3004  \u001b[0m | \u001b[0m 10.97   \u001b[0m |\n",
      "| \u001b[0m 368     \u001b[0m | \u001b[0m-7.677   \u001b[0m | \u001b[0m-1.67    \u001b[0m | \u001b[0m-3.219   \u001b[0m | \u001b[0m-0.2862  \u001b[0m | \u001b[0m 10.71   \u001b[0m |\n",
      "| \u001b[0m 369     \u001b[0m | \u001b[0m-7.808   \u001b[0m | \u001b[0m-1.652   \u001b[0m | \u001b[0m-3.223   \u001b[0m | \u001b[0m-0.282   \u001b[0m | \u001b[0m 10.7    \u001b[0m |\n",
      "| \u001b[0m 370     \u001b[0m | \u001b[0m-7.224   \u001b[0m | \u001b[0m-1.648   \u001b[0m | \u001b[0m-3.224   \u001b[0m | \u001b[0m-0.2787  \u001b[0m | \u001b[0m 10.7    \u001b[0m |\n",
      "| \u001b[0m 371     \u001b[0m | \u001b[0m-7.27    \u001b[0m | \u001b[0m-1.589   \u001b[0m | \u001b[0m-3.234   \u001b[0m | \u001b[0m-0.2703  \u001b[0m | \u001b[0m 10.66   \u001b[0m |\n",
      "| \u001b[0m 372     \u001b[0m | \u001b[0m-7.988   \u001b[0m | \u001b[0m-1.524   \u001b[0m | \u001b[0m-3.247   \u001b[0m | \u001b[0m-0.2578  \u001b[0m | \u001b[0m 10.63   \u001b[0m |\n",
      "| \u001b[0m 373     \u001b[0m | \u001b[0m-7.951   \u001b[0m | \u001b[0m-1.55    \u001b[0m | \u001b[0m-3.242   \u001b[0m | \u001b[0m-0.2646  \u001b[0m | \u001b[0m 10.64   \u001b[0m |\n",
      "| \u001b[0m 374     \u001b[0m | \u001b[0m-7.679   \u001b[0m | \u001b[0m-1.567   \u001b[0m | \u001b[0m-3.237   \u001b[0m | \u001b[0m-0.2704  \u001b[0m | \u001b[0m 10.65   \u001b[0m |\n",
      "| \u001b[0m 375     \u001b[0m | \u001b[0m-7.972   \u001b[0m | \u001b[0m-1.551   \u001b[0m | \u001b[0m-3.241   \u001b[0m | \u001b[0m-0.2665  \u001b[0m | \u001b[0m 10.64   \u001b[0m |\n",
      "| \u001b[95m 376     \u001b[0m | \u001b[95m-7.031   \u001b[0m | \u001b[95m-1.569   \u001b[0m | \u001b[95m-3.236   \u001b[0m | \u001b[95m-0.2707  \u001b[0m | \u001b[95m 10.65   \u001b[0m |\n",
      "| \u001b[0m 377     \u001b[0m | \u001b[0m-8.338   \u001b[0m | \u001b[0m-1.491   \u001b[0m | \u001b[0m-3.253   \u001b[0m | \u001b[0m-0.2535  \u001b[0m | \u001b[0m 10.61   \u001b[0m |\n",
      "| \u001b[0m 378     \u001b[0m | \u001b[0m-7.608   \u001b[0m | \u001b[0m-1.567   \u001b[0m | \u001b[0m-3.232   \u001b[0m | \u001b[0m-0.2839  \u001b[0m | \u001b[0m 10.65   \u001b[0m |\n",
      "| \u001b[0m 379     \u001b[0m | \u001b[0m-7.419   \u001b[0m | \u001b[0m-1.547   \u001b[0m | \u001b[0m-3.238   \u001b[0m | \u001b[0m-0.2773  \u001b[0m | \u001b[0m 10.63   \u001b[0m |\n",
      "| \u001b[0m 380     \u001b[0m | \u001b[0m-7.559   \u001b[0m | \u001b[0m-1.509   \u001b[0m | \u001b[0m-3.246   \u001b[0m | \u001b[0m-0.2687  \u001b[0m | \u001b[0m 10.61   \u001b[0m |\n",
      "| \u001b[0m 381     \u001b[0m | \u001b[0m-8.056   \u001b[0m | \u001b[0m-1.484   \u001b[0m | \u001b[0m-3.252   \u001b[0m | \u001b[0m-0.2614  \u001b[0m | \u001b[0m 10.6    \u001b[0m |\n",
      "| \u001b[0m 382     \u001b[0m | \u001b[0m-7.823   \u001b[0m | \u001b[0m-1.514   \u001b[0m | \u001b[0m-3.243   \u001b[0m | \u001b[0m-0.2722  \u001b[0m | \u001b[0m 10.62   \u001b[0m |\n",
      "| \u001b[95m 383     \u001b[0m | \u001b[95m-7.025   \u001b[0m | \u001b[95m-1.517   \u001b[0m | \u001b[95m-3.243   \u001b[0m | \u001b[95m-0.2726  \u001b[0m | \u001b[95m 10.62   \u001b[0m |\n",
      "| \u001b[0m 384     \u001b[0m | \u001b[0m-7.723   \u001b[0m | \u001b[0m-1.446   \u001b[0m | \u001b[0m-3.261   \u001b[0m | \u001b[0m-0.2537  \u001b[0m | \u001b[0m 10.57   \u001b[0m |\n",
      "| \u001b[0m 385     \u001b[0m | \u001b[0m-8.137   \u001b[0m | \u001b[0m-1.441   \u001b[0m | \u001b[0m-3.263   \u001b[0m | \u001b[0m-0.2507  \u001b[0m | \u001b[0m 10.57   \u001b[0m |\n",
      "| \u001b[0m 386     \u001b[0m | \u001b[0m-7.88    \u001b[0m | \u001b[0m-1.483   \u001b[0m | \u001b[0m-3.249   \u001b[0m | \u001b[0m-0.2688  \u001b[0m | \u001b[0m 10.6    \u001b[0m |\n",
      "| \u001b[0m 387     \u001b[0m | \u001b[0m-8.121   \u001b[0m | \u001b[0m-1.492   \u001b[0m | \u001b[0m-3.247   \u001b[0m | \u001b[0m-0.2713  \u001b[0m | \u001b[0m 10.6    \u001b[0m |\n",
      "| \u001b[0m 388     \u001b[0m | \u001b[0m-7.737   \u001b[0m | \u001b[0m-1.522   \u001b[0m | \u001b[0m-3.239   \u001b[0m | \u001b[0m-0.2832  \u001b[0m | \u001b[0m 10.61   \u001b[0m |\n",
      "| \u001b[0m 389     \u001b[0m | \u001b[0m-9.363   \u001b[0m | \u001b[0m-1.517   \u001b[0m | \u001b[0m-3.241   \u001b[0m | \u001b[0m-0.2801  \u001b[0m | \u001b[0m 10.61   \u001b[0m |\n",
      "| \u001b[0m 390     \u001b[0m | \u001b[0m-7.591   \u001b[0m | \u001b[0m-1.678   \u001b[0m | \u001b[0m-3.158   \u001b[0m | \u001b[0m-0.4087  \u001b[0m | \u001b[0m 10.69   \u001b[0m |\n",
      "| \u001b[0m 391     \u001b[0m | \u001b[0m-7.505   \u001b[0m | \u001b[0m-1.681   \u001b[0m | \u001b[0m-3.14    \u001b[0m | \u001b[0m-0.4429  \u001b[0m | \u001b[0m 10.69   \u001b[0m |\n",
      "| \u001b[0m 392     \u001b[0m | \u001b[0m-8.02    \u001b[0m | \u001b[0m-1.686   \u001b[0m | \u001b[0m-3.117   \u001b[0m | \u001b[0m-0.4849  \u001b[0m | \u001b[0m 10.68   \u001b[0m |\n",
      "| \u001b[0m 393     \u001b[0m | \u001b[0m-7.174   \u001b[0m | \u001b[0m-1.683   \u001b[0m | \u001b[0m-3.136   \u001b[0m | \u001b[0m-0.4529  \u001b[0m | \u001b[0m 10.68   \u001b[0m |\n",
      "| \u001b[0m 394     \u001b[0m | \u001b[0m-7.268   \u001b[0m | \u001b[0m-1.692   \u001b[0m | \u001b[0m-3.097   \u001b[0m | \u001b[0m-0.535   \u001b[0m | \u001b[0m 10.68   \u001b[0m |\n",
      "| \u001b[0m 395     \u001b[0m | \u001b[0m-7.802   \u001b[0m | \u001b[0m-1.703   \u001b[0m | \u001b[0m-3.058   \u001b[0m | \u001b[0m-0.6207  \u001b[0m | \u001b[0m 10.67   \u001b[0m |\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m~/miniconda3/envs/main2/lib/python3.7/site-packages/bayes_opt/bayesian_optimization.py\u001b[0m in \u001b[0;36mmaximize\u001b[0;34m(self, init_points, n_iter, acq, kappa, xi, **gp_params)\u001b[0m\n\u001b[1;32m    168\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m                 \u001b[0mx_probe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_queue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/main2/lib/python3.7/site-packages/bayes_opt/bayesian_optimization.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Queue is empty, no more objects to retrieve.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m         \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_queue\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mStopIteration\u001b[0m: Queue is empty, no more objects to retrieve.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-166c5c45facc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaximize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minit_points\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcur_std\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2500\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/main2/lib/python3.7/site-packages/bayes_opt/bayesian_optimization.py\u001b[0m in \u001b[0;36mmaximize\u001b[0;34m(self, init_points, n_iter, acq, kappa, xi, **gp_params)\u001b[0m\n\u001b[1;32m    169\u001b[0m                 \u001b[0mx_probe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_queue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m                 \u001b[0mx_probe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuggest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mutil\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    172\u001b[0m                 \u001b[0miteration\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/main2/lib/python3.7/site-packages/bayes_opt/bayesian_optimization.py\u001b[0m in \u001b[0;36msuggest\u001b[0;34m(self, utility_function)\u001b[0m\n\u001b[1;32m    130\u001b[0m             \u001b[0my_max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m             \u001b[0mbounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbounds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m             \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_random_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m         )\n\u001b[1;32m    134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/main2/lib/python3.7/site-packages/bayes_opt/util.py\u001b[0m in \u001b[0;36macq_max\u001b[0;34m(ac, gp, y_max, bounds, random_state, n_warmup, n_iter)\u001b[0m\n\u001b[1;32m     44\u001b[0m     x_tries = random_state.uniform(bounds[:, 0], bounds[:, 1],\n\u001b[1;32m     45\u001b[0m                                    size=(n_warmup, bounds.shape[0]))\n\u001b[0;32m---> 46\u001b[0;31m     \u001b[0mys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mac\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_tries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0mx_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_tries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[0mmax_acq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/main2/lib/python3.7/site-packages/bayes_opt/util.py\u001b[0m in \u001b[0;36mutility\u001b[0;34m(self, x, gp, y_max)\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mutility\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'ucb'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ucb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkappa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     98\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'ei'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ei\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/main2/lib/python3.7/site-packages/bayes_opt/util.py\u001b[0m in \u001b[0;36m_ucb\u001b[0;34m(x, gp, kappa)\u001b[0m\n\u001b[1;32m    105\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcatch_warnings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m             \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimplefilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 107\u001b[0;31m             \u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_std\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmean\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mkappa\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mstd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/main2/lib/python3.7/site-packages/sklearn/gaussian_process/_gpr.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X, return_std, return_cov)\u001b[0m\n\u001b[1;32m    335\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0my_mean\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Predict based on GP posterior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 337\u001b[0;31m             \u001b[0mK_trans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_train_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    338\u001b[0m             \u001b[0my_mean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mK_trans\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malpha_\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Line 4 (y_mean = f_star)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m             \u001b[0my_mean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y_train_mean\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my_mean\u001b[0m  \u001b[0;31m# undo normal.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/main2/lib/python3.7/site-packages/sklearn/gaussian_process/kernels.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, X, Y, eval_gradient)\u001b[0m\n\u001b[1;32m   1412\u001b[0m             \u001b[0mK\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1.\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mK\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1413\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnu\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2.5\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1414\u001b[0;31m             \u001b[0mK\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdists\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1415\u001b[0m             \u001b[0mK\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1.\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mK\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mK\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m3.0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mK\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1416\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# general case; expensive to evaluate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "optimizer.maximize(init_points=4**len(cur_std), n_iter=300+4**len(cur_std), alpha=1e-2, normalize_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'target': -7.024937629699707,\n",
       " 'params': {'s0': -1.5170019728292918,\n",
       "  's1': -3.2433987013595225,\n",
       "  's2': -0.27262712630267544,\n",
       "  's3': 10.6150648487571}}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimizer.max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 30)\n",
      "(30,)\n",
      "(30, 30)\n",
      "(30,)\n",
      "(30, 30)\n",
      "(30,)\n",
      "(30, 1)\n",
      "(1,)\n"
     ]
    }
   ],
   "source": [
    "for l1 in init_params:\n",
    "    if (len(l1)) == 0: continue\n",
    "    for l2 in l1:\n",
    "        print(l2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_data = np.array(\n",
    "[[t['params']['s%d'%(i,)] for i in range(len(cur_std))] + [t['target']]\n",
    "for t in optimizer.res])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('hidden={}_layers={}_results.npy'.format(args.hidden_dim, args.layers), simple_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.gaussian_process import GaussianProcessRegressor, kernels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "gp = GaussianProcessRegressor(alpha=1e-2, n_restarts_optimizer=20, normalize_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.440377235412598"
      ]
     },
     "execution_count": 568,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# simple_data[:, -1].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianProcessRegressor(alpha=0.01, copy_X_train=True, kernel=None,\n",
       "                         n_restarts_optimizer=20, normalize_y=True,\n",
       "                         optimizer='fmin_l_bfgs_b', random_state=None)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.fit(simple_data[:, :-1], simple_data[:, -1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  9.00996737,  -2.20082525,   3.21046382, -10.63603169])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_data[:, :-1][gp.predict(simple_data[:, :-1]).argmin()]"
   ]
  }
 ],
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